New Science Advances paper w/ Jennifer Candipan:
Racial change doesn’t just reshape neighborhoods, it reshapes school punishment. We show Black–White suspension disparities grow in places where Black populations are changing, especially in majority White, suburban, & rural areas.
Welcome to NJ! Yep, lots of macro-segregation and local govt fragmentation over here
New piece in the March issue of Social Forces tracing 120 years of segregation.
We note the emergence of a new kind of segregation starting the 1950s: macro-segregation.
doi.org/10.1093/sf/s...
@sfjournal.bsky.social
Great new housing papers:
"Assisted Housing and Changes in Household Composition" by Kristin Perkins shows that receiving federal or state housing assistance stabilizes families
doi.org/10.1177/2378...
[New paper!] Mobility data are incredibly powerful, but also come with a long list of well-known biases (sampling, coverage, demographics, behavioral, etc.). In the paper, we survey them all and then zoom in on one that has been surprisingly underexplored: temporal bias.
arxiv.org/abs/2601.22330
This is a belated post about our paper in @poqjournal.bsky.social.
We analyzed 100 survey experiments fielded by TESS (tessexperiments.org), using only information from the proposals to identify intended hypotheses.
Here are some of the things we learned:
Young people's moves contribute to inequality by sorting people with high earning potential to areas with high pay premiums. onlinelibrary.wiley.com/doi/10.1111/...
Great new work by @xiaoweixu.bsky.social based on English admin data. @theifs.bsky.social @sriucl.bsky.social @cepeo-ucl.bsky.social
Really thoughtful cross-national qualitative work by @elenaah.bsky.social that highlights how different policy contexts (re: employment, housing, poverty alleviation) translate into different lived experiences for young people experiencing insecurity.
New in @socprobsjournal.bsky.social w/ @estelabdiaz.bsky.social: the rapid growth of the admissions consulting industry has raised questions about inequality, privilege, and merit. We combine two original data sources to ask how consultants make sense of their work.
academic.oup.com/socpro/advan...
🚨 New paper: “Does Rent Control Turn Tenants Into NIMBYs?” in the Journal of Politics (JOP)
(joint work with @anselmhager.bsky.social and @hannohilbig.bsky.social)
👉 Have a look over here: www.journals.uchicago.edu/doi/10.1086/...
Most important findings in this thread:
1/11
Teaching a class on panel data and all of my good examples are US-only data. What are your go-to examples for panel and / or multilevel datasets that aren't just the US? Can be at any level (individual, national, organizational, etc), and the clustering can be across either space or time (or both).
A neat paper in political economy that provides a different take on establishing causal relationships is Acharya, Blackwell, Sen 2016 - they use falsification testing to try to rule out causal mechanisms (see also their book Deep Roots): www.journals.uchicago.edu/doi/10.1086/...
Thanks for your causal inference readings suggestions! Next Q: what's a paper that (mostly) convinced you of causal relationships *without* an exogenous shock? My students seek examples of good work when using an IV or RD or something isn't an option. (Again, I appreciate RTs to crowd source.)
Happy to say that the reading/teaching guide for Unequal Lessons is now live! It's chockful of prompts and resources for folks who are using the book in reading groups or classes.
You can find it on the NYU Press book page or access it directly here:
nyu.app.box.com/s/hcffzukiyq...
I tried out a new idea in my Race, Place & Inequality class this term: students formed small reading groups and spent the semester reading a book on a topic not already covered on the syllabus (in addition to all the regular readings). A quick list of the books they read (many released this year):
Crowdsourced tips from people who've been doing this a lot longer than me - thanks to my collaborators and advisors who provided some of the baseline tips for this document, especially @jenjennings.bsky.social! (mostly sharing publicly so I remember I wrote this)
If you are like me, you sometimes table big research projects to work on more urgent things.
📝 Here is a guide for coming BACK to big research projects (in empirical social science) after some time off. Wrote this note for myself after doing this one too many times: docs.google.com/document/d/1...
About to kick off a peer review workshop with our brilliant @sriucl.bsky.social PhD students right now. Thanks to my colleague Alina Pelikh for hosting and I wish something like this was available when I started out.
Thank you!
Agreed, we do our best with the data we were able to access - there are certainly assumptions and mechanisms that should be tested in future analyses. We cite your work in our paper as a great example of that!
Also, feel free to reach out to me at angelamli [at] princeton [dot] edu if you'd like a copy of the paper for review and can't access it at the link above!
Abstract for "The truly isolated: Spatial isolation of advantage in the United States" by Shannon Rieger, Angela Li, and Patrick Sharkey, published at Urban Studies
👉 Our new paper uses daily mobility data to show that spatial isolation is much more common today among those living in advantaged neighborhoods than the converse.
👩🏻💻 Lots of massive data wrangling and careful assumptions about mobility data needed - but check it out here! doi.org/10.1177/0042...
🔎 We used the results to make an interactive web map that allows people to look up how the property wealth in their own metro area (or any other) is fragmented across different local municipalities. This tool visualizes tax base fragmentation across the US—check it out: www.taxbasefragmentation.net
🚨We analyzed 138 million geocoded property tax records to quantify how municipal boundaries spatially overlap onto economic segregation in every US metro area—creating disparities in localities’ ability to fund public goods. And we made an interactive map of our results! [1/16]
What are Americans’ perceptions of immigrants’ politics? How do beliefs about whether newcomers are future allies or adversaries shape immigration attitudes? A new #AJS article shows that perceived partisan (mis)alignment powerfully informs US public opinion on immigration.
Very cool article about how our fragmentation of local tax bases allows some suburbs to effectively act as tax havens at the expense of central metro areas.
(The link preview is bad, but it's:
Tax base fragmentation as a dimension of metropolitan inequality
by Manduca, Highsmith, & Waggoner)
I'm facilitating a causal inference reading group next semester for Sociology PhD students. (I will also be learning!) If there are (1) pedagogical articles or (2) empirical examples in soc that you ❤️, will you share in the comments? [And please RT to help me crowd-source!]
- Sharkey 2010: efft of violence on kids' test scores pubmed.ncbi.nlm.nih.gov/20547862/
- Zang et al. 2023: efft of older sibling on younger sibling's academic outcomes pmc.ncbi.nlm.nih.gov/articles/PMC...
- @tomdee.bsky.social 2024: efft of imm raids on absenteeism edworkingpapers.com/ai25-1202
In my experience, excellent applied examples of causal inference in Soc tend to have 1) a *real* shock/change/cutoff in the world (ie. violence, program cutoffs, sudden change in policy) 2) a robust data infrastructure to identify effects. Some examples (mostly w/ed outcomes) below...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).
Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.
A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
Ever stared at a table of regression coefficients & wondered what you're doing with your life?
Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
