Ea Blaabæk

❓How do mothers’ and fathers’ paid work lives unfold together after the birth of their first child in DE?

✨ This new preprint takes a longitudinal, dynamic, couple-level perspective.

✨ Big differences by East & West German background, periods and couple-level education

🔗 doi.org/10.31235/osf...

The Conference Addressing Social Inequalities in the Global North and South On behalf of the Organising Committee, it is an honour to welcome you to the RC28 Spring Meeting 2026. This academic meeting is proposed a...

Next RC28 Spring Meeting will be 20-22 May in Seville.
Submissions until 10 Dec here: eventos.upo.es/137585/detai...

If you're wondering if this is your conference, have a look at what we publish in our journal Research in Social Stratification and Mobility: www.sciencedirect.com/journal/rese...

Lab Study in Berlin/Madrid on School Meritocracy:
Effort🔨(🧠objective cognitive effort / 👩‍🏫teacher-perceived effort) → + grades & inequality. Net of IQ, High-SES👨‍🎓 + effort & GPA returns when lazy. Low-SES👨‍🎓 - effort; + GPA returns if hardworking. WP👇 Effort-Project econpapers.repec.org/RePEc:fir:ec...

🚨 New paper: Who climbs the Ivory Tower? 🏛️ Together with Nicolai Borgen and Astrid Sandsør (@astridsandsor.bsky.social), we find that the chances of becoming a professor differ enormously by family background. Here’s what we find 👇

journals.sagepub.com/doi/full/10....

Paper by Cozzani et al. explores a possible mechanism underlying ART birth disparities & highlights that these disparities do not appear to arise from treatment success, at least when treatments are performed in widely subsidized public context in Italy. www.demographic-research.org/articles/vol...

Elucidating some common biases in randomized controlled trials using directed acyclic graphs Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial’s specific imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections, such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained

Table 1 showing the Identifiability of target estimands depending on whether there is blinding, full compliance, and no drop-out

An example DAG from the paper. Fig. 4: A blinded trial with noncompliance. U are unobserved confounders, Z is treatment assignment, C is compliance, X is the realized treatment, S is the subject's physical and mental health status, Xself and Xcln are the treatment that the participant and the clinician believed the participant received, Y is the outcome.

Just finished reading this *excellent* article by Gabriel et al. which discusses which effects can be identified in randomized controlled trials. With DAGs!>

link.springer.com/article/10.1...

Studying a survey of Danish mothers' pre-birth beliefs about how their career will unfold finds they accurately anticipate their eventual return to work but underestimate the duration of the career interruption, from Andrew Caplin, Søren Leth-Petersen, and Chris... https://www.nber.org/papers/w34289

How are cultural tastes stratified? Evidence from library borrowing for the entire population of Denmark Abstract. Research shows that cultural tastes are socially stratified. Yet, most of this research relies on small-sample surveys and includes only a few di

➡️New paper out with amazing data: library borrowings of the entire population of Denmark!
If cultural tastes are always stratified, the type of stratification depends on the wider inequalities within a context
#EH_Blaabæk #S_Friedman #MM_Jæger #A_Reeves

academic.oup.com/esr/advance-...

This is a great paper!

New paper! Led by the brilliant @blaabaek.bsky.social and based on library borrowing data for the entire 🇩🇰 population. Thread below

Teacher bias by student SES or measurement error in ability? A cautionary tale for observational studies Teachers act as judges of academic merit, but unfair evaluations beyond students' true abilities may perpetuate inequality based on socioeconomic stat…

Interesting paper on teacher bias by student SES www.sciencedirect.com/science/arti...

As a side note: We struggled with how to meassure "highbrow" tastes without having to simply pick what we thought of as "fancy"

In the end, we asked librarians and literary critics to rank genres. Personally, I think this is neat to capture taste hierarchies as expressed in the empirical context

In a time of political polarization, it’s worth asking if
culture can bring people together across social divides?
👉 Crime fiction might be such a place for common ground

Public libraries are for everyone. But if some groups don’t use them, we should consider:
📚Do libraries appeal equally to all?

@madsjaeger.bsky.social @samfriedman.bsky.social @aaronreeves.bsky.social

👩‍🎓 People with higher education & wealth use libraries much more
💼 Differences across income and occupations are smaller
‍🕵️‍♀️ Everyone loves crime novels!
🎨 But the highly educated & wealthy are also drawn to “highbrow” genres

How are cultural tastes stratified? Evidence from library borrowing for the entire population of Denmark Abstract. Research shows that cultural tastes are socially stratified. Yet, most of this research relies on small-sample surveys and includes only a few di

Even in Denmark, cultural tastes are socially stratified. With some quite cool library data we find strong gradients by education and wealth - important as wealth is rarely studied in cultural stratification research

doi.org/10.1093/esr/...
OA: osf.io/ar2xc_v2/dow...
@europeansocreview.bsky.social

Teacher bias or unobserved ability? @ssreditorial.bsky.social paper w/ @marespadafor.bsky.social Test score error & omitted behavior = 🐘 in the (class)room to identify SES discrimination. Still, beyond "true ability", well-off (low-performing) kids get higher teacher ratings: doi.org/10.1016/j.ss...

Research on fertility and families enhanced with new harmonised cohort data | CLS Harmonised data on the fertility histories of four British cohorts are now available for the scientific community to download from the UK Data Service.

NEW DATA! Harmonised fertility histories of four British cohorts – born in 1946, 1958, 1970 and 1989-90 – are now available for the scientific community to download from the @ukdataservice.bsky.social

Find out more on the CLS website – bit.ly/42CN0qr

How are cultural tastes stratified? Evidence from library borrowing for the entire population of Denmark Abstract. Research shows that cultural tastes are socially stratified. Yet, most of this research relies on small-sample surveys and includes only a few di

"We find considerable social stratification in library borrowing and in highbrow literary tastes by wealth and education, but not by income and occupation." academic.oup.com/esr/advance-...

How Are Cultural Tastes Stratified? Evidence from Library Borrowing for the Entire Population of Denmark Research shows that cultural tastes are socially stratified. Yet, most of this research relies on small-sample surveys and includes only a few dimensions of stratification. To
#sociology link

How are cultural tastes stratified? Evidence from library borrowing for the entire population of Denmark Abstract. Research shows that cultural tastes are socially stratified. Yet, most of this research relies on small-sample surveys and includes only a few di

Interesting paper by @blaabaek.bsky.social and colleagues about cultural tastes using libary borrowing data in Denmark academic.oup.com/esr/advance-...

More evidence, on top of Baby's First Years experiment, that cash alone in early childhood does not improve children's early developmental trajectory.

In contrast, there is strong evidence that policies that improve children's care experiences do improve their early development & adult outcomes.

1 year left of my @erc.europa.eu grant on siblings and social mobility! It has been a blast. Below some of the reserach conducted by the outstanding postdocs

Jung In, JMF, onlinelibrary.wiley.com/doi/10.1111/...

Jesper Birkelund, BJS, onlinelibrary.wiley.com/doi/10.1111/...

1/2

#sociology

interesting paper on publication based tenure track system using a reform in Italy in 2010
papers.ssrn.com/sol3/papers....

We invest 9X less per child-year in care & education in the first 5 years of life than the next 13.

This gap in public investment is why K12 is free for parents & early care & education is expensive.
www.hamiltonproject.org/publication/...

Unionized careers boost up wealth. Very interesting article by Purdue's Alec Rhodes:

academic.oup.com/sf/advance-a....

NEW: Samuel Coavoux, Abel Aussant, "Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity" sociologicalscience.com/streaming-pl...

Self-care is overrated—helping others has the biggest benefits for everyone involved

A new 2-week intervention finds that helping others improves well-being more than "self-kindness", with benefits for depressed mood, anxiety, and loneliness due to social connection psycnet.apa.org/record/2026-...

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...

The Different Sources of Intergenerational Income Mobility in High‐ and Low‐Income Families This paper studies intergenerational income mobility using register data for 630,000 Danish children and their parents. We document substantial mobility differences across parents' income levels. Dec...

Happy to see my paper with @andershtrolle.bsky.social on "The different sources of intergenerational income mobility in high- and low-income families" online before print in Journal of Applied Econometrics:
onlinelibrary.wiley.com/doi/10.1002/...

Good to see growing support for abduction or 'inference to the best explanation', recently by Spirling and Stewart in @thejop.bsky.social. This is...

www.journals.uchicago.edu/doi/full/10....