David Slichter

The whole point of "correlation is not causation" is that correlations are usually consistent with multiple interpretations. But authors who are skillful at framing correlations can often persuade an audience into fixating to an unwarranted extent on one specific interpretation. (end)

The thing I like about this is that it drives home the extent to which papers can bias you in the direction of believing an IV simply by having placed you in the frame of mind of interpreting a correlation a certain way. (6/n)

Once you reveal the actual X from that paper, you can explain that this won a Nobel Prize. Enjoy the resulting facial expressions. (5/n)

For instance, try asking a roomful of undergrads or early PhD students why they think mortality rates among early European settlers are correlated with GDP per capita in 1995. (4/n)

A fun classroom exercise when teaching IV is to tell the class Z and Y for some famous papers, and ask them to guess what X is. The results are usually entertaining. (3/n)

For instance, if asked, "why do you think that the kids who were randomly admitted to a certain middle school wind up with different test scores from the kids who were randomly not admitted?" people will realize that this probably means the school affects scores. (2/n)

My personal rule for IVs is that, if there's really only one reason why Z and Y are correlated, then you should be able to tell me Z and Y, and I'd be able to figure out what X is without you telling me. (1/n)

My vision: Authors make the case that it is a priori plausible that beta could be at least as small as b, but also plausible that it could at least as large as b', then argue that their sampling + modeling error is small relative to b'-b. Refs' job is to evaluate these arguments.

Distribution of z-stats on Medline, via onlinelibrary.wiley.com/doi/10.1111/...

Think of assessing the traditional unbiasedness condition E(estimate|truth) = truth for an estimator where you estimate some true parameter by randomly sampling from the space of published estimates of it.

Without this, the current standard is to treat null results are presumptively imprecise, and non-null results as presumptively precise. But you want to judge precision only from the SEs, while significance is as much about the point estimates as about the SEs.

Yes, imprecise results shouldn't be publishable. The issue is that there is currently no expectation for authors to explain what constitutes a priori uncertainty based on theory, existing evidence, or auxiliary info.

Making it permissible to say that coffee affects dementia risk, but not permissible to say that it doesn't, means that the public cannot tell from the literature whether coffee affects dementia risk or not.

I think the right paradigm is that SEs + modeling error need to be small compared with a priori uncertainty about a parameter value. This means you learn something. If you select for estimates which disagree with priors, then you are selecting for publishing things which are likely to be false.

If these properties don't matter, why care about endogeneity or measurement error or other standard concerns?

Ideally, published estimates would be unbiased in the sense of E(true parameter | published parameter) = published parameter. That's unrealistic because it requires shrinking towards correct priors. More realistically, E(published estimate | true parameter) = true parameter would be fine.

Definitely a step in the right direction, but the main issue with a cap is that better students take some classes than others. There's no reason Math 55 should have the same cap as Math 21.

My opinion is that nobody should pay attention to any of the Nobels.

I have simply accepted that publication in an elite journal means that the paper was interesting, not that it was correct. There are lots of empirical indicators that peer review does not sort more reliable findings to more prestigious journals.

A Systematic Review of Pre-primary Education Interventions in Africa Abstract. In this paper, we synthesise evidence on the effects of pre-primary education interventions on children's cognitive and socioemotional developmen

Article link: doi.org/10.1093/jae/...

Authors of provisionally accepted proposals must then submit a full version of their articles no later than May 15, after which the manuscript will undergo peer review before final acceptance.

Submissions (extended abstracts are accepted, full papers preferred) should be emailed to rle@binghamton.edu. Paper proposals are due January 15, 2026. Authors will be notified of preliminary acceptance by February 9.

RLE was affiliated with IZA from 2007 to 2025. Following the closure of IZA, the series will be housed at Binghamton University starting in 2026. I'll be joining Sol Polachek as co-editor.

RLE is an annual series which publishes new peer-reviewed research on important and policy-relevant topics in labor economics. Since its inception, RLE has published over 400 articles on many themes, with authors ranging from grad students to Nobel Prize and John Bates Clark award winners.

Suitable articles include, but are not limited to:
• Meta-analyses
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🚨 CALL FOR PAPERS 🚨
Research In Labor Economics (rle.iza.org) is planning a volume featuring critical analyses of applied economics research. We are soliciting up to ten new papers that rigorously review an applied microeconomics topic, area, or methodology. #econsky

Heinz beanz, eaten directly from the can

This affects maybe 5-10% of econ papers.

Perhaps the field of economics should take that as some kind of warning.

One fun thing about forecasting is having a record of what you find surprising about a paper's results. My biggest surprise about this interesting new paper is that I figured subfield experts would know more than non-experts. But no, apparently they don't.