nhaliday + outliers   17

The weirdest people in the world?
Abstract: Behavioral scientists routinely publish broad claims about human psychology and behavior in the world’s top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers – often implicitly – assume that either there is little variation across human populations, or that these “standard subjects” are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species – frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior – hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges.
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november 2017 by nhaliday
Inherited Trust and Growth - American Economic Association
This paper develops a new method to uncover the causal effect of trust on economic growth by focusing on the inherited component of trust and its time variation. We show that inherited trust of descendants of US immigrants is significantly influenced by the country of origin and the timing of arrival of their forebears. We thus use the inherited trust of descendants of US immigrants as a time-varying measure of inherited trust in their country of origin. This strategy allows to identify the sizeable causal impact of inherited trust on worldwide growth during the twentieth century by controlling for country fixed effects. (JEL N11, N12, N31, N32, O47, Z13)

key data:
Table 1, Figure 1, Figure 3, Figure 4

Trust Assimilation in the United States, Bryan Caplan: http://econlog.econlib.org/archives/2017/05/trust_assimilat.html

How Durable are Social Norms? Immigrant Trust and Generosity in 132 Countries: http://www.nber.org/papers/w19855
We find that migrants tend to make social trust assessments that mainly reflect conditions in the country where they now live, but they also reveal a significant influence from their countries of origin. The latter effect is one-third as important as the effect of local conditions. We also find that the altruistic behavior of migrants, as measured by the frequency of their donations in their new countries, is strongly determined by social norms in their new countries, while also retaining some effect of the levels of generosity found in their birth countries. To show that the durability of social norms is not simply due to a failure to recognize new circumstances, we demonstrate that there are no footprint effects for immigrants’ confidence in political institutions. Taken together, these findings support the notion that social norms are deeply rooted in long-standing cultures, yet are nonetheless subject to adaptation when there are major changes in the surrounding circumstances and environment.

The autocratic roots of social distrust: http://sci-hub.tw/https://www.sciencedirect.com/science/article/pii/S0147596717300951
This paper identifies a new source of social distrust: an individual’s autocratic
origin.
 Individuals whose ancestors migrated from countries with higher autocracy
levels are less likely to trust others and to vote in presidential elections in the
U.S.
 The impact of autocratic culture on trust lasts for at least three generations
whereas the impact on voting disappears after one generation.
 The results are not driven by selection into migration or other factors such as the
GDP, education, or the strength of family ties in home countries in the U.S.
 Autocratic culture also has similar impacts on trust and voting across Europe.
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july 2017 by nhaliday
Econometric Modeling as Junk Science
The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics: https://www.aeaweb.org/articles?id=10.1257/jep.24.2.3

On data, experiments, incentives and highly unconvincing research – papers and hot beverages: https://papersandhotbeverages.wordpress.com/2015/10/31/on-data-experiments-incentives-and-highly-unconvincing-research/
In my view, it has just to do with the fact that academia is a peer monitored organization. In the case of (bad) data collection papers, issues related to measurement are typically boring. They are relegated to appendices, no one really has an incentive to monitor it seriously. The problem is similar in formal theory: no one really goes through the algebra in detail, but it is in principle feasible to do it, and, actually, sometimes these errors are detected. If discussing the algebra of a proof is almost unthinkable in a seminar, going into the details of data collection, measurement and aggregation is not only hard to imagine, but probably intrinsically infeasible.

Something different happens for the experimentalist people. As I was saying, I feel we have come to a point in which many papers are evaluated based on the cleverness and originality of the research design (“Using the World Cup qualifiers as an instrument for patriotism!? Woaw! how cool/crazy is that! I wish I had had that idea”). The sexiness of the identification strategy has too often become a goal in itself. When your peers monitor you paying more attention to the originality of the identification strategy than to the research question, you probably have an incentive to mine reality for ever crazier discontinuities. It is true methodologists have been criticized in the past for analogous reasons, such as being guided by the desire to increase mathematical complexity without a clear benefit. But, if you work with pure formal theory or statistical theory, your work is not meant to immediately answer question about the real world, but instead to serve other researchers in their quest. This is something that can, in general, not be said of applied CI work.

https://twitter.com/pseudoerasmus/status/662007951415238656
This post should have been entitled “Zombies who only think of their next cool IV fix”
https://twitter.com/pseudoerasmus/status/662692917069422592
massive lust for quasi-natural experiments, regression discontinuities
barely matters if the effects are not all that big
I suppose even the best of things must reach their decadent phase; methodological innov. to manias……

https://twitter.com/cblatts/status/920988530788130816
Following this "collapse of small-N social psych results" business, where do I predict econ will collapse? I see two main contenders.
One is lab studies. I dallied with these a few years ago in a Kenya lab. We ran several pilots of N=200 to figure out the best way to treat
and to measure the outcome. Every pilot gave us a different stat sig result. I could have written six papers concluding different things.
I gave up more skeptical of these lab studies than ever before. The second contender is the long run impacts literature in economic history
We should be very suspicious since we never see a paper showing that a historical event had no effect on modern day institutions or dvpt.
On the one hand I find these studies fun, fascinating, and probably true in a broad sense. They usually reinforce a widely believed history
argument with interesting data and a cute empirical strategy. But I don't think anyone believes the standard errors. There's probably a HUGE
problem of nonsignificant results staying in the file drawer. Also, there are probably data problems that don't get revealed, as we see with
the recent Piketty paper (http://marginalrevolution.com/marginalrevolution/2017/10/pikettys-data-reliable.html). So I take that literature with a vat of salt, even if I enjoy and admire the works
I used to think field experiments would show little consistency in results across place. That external validity concerns would be fatal.
In fact the results across different samples and places have proven surprisingly similar across places, and added a lot to general theory
Last, I've come to believe there is no such thing as a useful instrumental variable. The ones that actually meet the exclusion restriction
are so weird & particular that the local treatment effect is likely far different from the average treatment effect in non-transparent ways.
Most of the other IVs don't plausibly meet the e clue ion restriction. I mean, we should be concerned when the IV estimate is always 10x
larger than the OLS coefficient. This I find myself much more persuaded by simple natural experiments that use OLS, diff in diff, or
discontinuities, alongside randomized trials.

What do others think are the cliffs in economics?
PS All of these apply to political science too. Though I have a special extra target in poli sci: survey experiments! A few are good. I like
Dan Corstange's work. But it feels like 60% of dissertations these days are experiments buried in a survey instrument that measure small
changes in response. These at least have large N. But these are just uncontrolled labs, with negligible external validity in my mind.
The good ones are good. This method has its uses. But it's being way over-applied. More people have to make big and risky investments in big
natural and field experiments. Time to raise expectations and ambitions. This expectation bar, not technical ability, is the big advantage
economists have over political scientists when they compete in the same space.
(Ok. So are there any friends and colleagues I haven't insulted this morning? Let me know and I'll try my best to fix it with a screed)

HOW MUCH SHOULD WE TRUST DIFFERENCES-IN-DIFFERENCES ESTIMATES?∗: https://economics.mit.edu/files/750
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the auto-correlation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre” and “post” period and explicitly takes into account the effective sample size works well even for small numbers of states.

‘METRICS MONDAY: 2SLS–CHRONICLE OF A DEATH FORETOLD: http://marcfbellemare.com/wordpress/12733
As it turns out, Young finds that
1. Conventional tests tend to overreject the null hypothesis that the 2SLS coefficient is equal to zero.
2. 2SLS estimates are falsely declared significant one third to one half of the time, depending on the method used for bootstrapping.
3. The 99-percent confidence intervals (CIs) of those 2SLS estimates include the OLS point estimate over 90 of the time. They include the full OLS 99-percent CI over 75 percent of the time.
4. 2SLS estimates are extremely sensitive to outliers. Removing simply one outlying cluster or observation, almost half of 2SLS results become insignificant. Things get worse when removing two outlying clusters or observations, as over 60 percent of 2SLS results then become insignificant.
5. Using a Durbin-Wu-Hausman test, less than 15 percent of regressions can reject the null that OLS estimates are unbiased at the 1-percent level.
6. 2SLS has considerably higher mean squared error than OLS.
7. In one third to one half of published results, the null that the IVs are totally irrelevant cannot be rejected, and so the correlation between the endogenous variable(s) and the IVs is due to finite sample correlation between them.
8. Finally, fewer than 10 percent of 2SLS estimates reject instrument irrelevance and the absence of OLS bias at the 1-percent level using a Durbin-Wu-Hausman test. It gets much worse–fewer than 5 percent–if you add in the requirement that the 2SLS CI that excludes the OLS estimate.

Methods Matter: P-Hacking and Causal Inference in Economics*: http://ftp.iza.org/dp11796.pdf
Applying multiple methods to 13,440 hypothesis tests reported in 25 top economics journals in 2015, we show that selective publication and p-hacking is a substantial problem in research employing DID and (in particular) IV. RCT and RDD are much less problematic. Almost 25% of claims of marginally significant results in IV papers are misleading.

https://twitter.com/NoamJStein/status/1040887307568664577
Ever since I learned social science is completely fake, I've had a lot more time to do stuff that matters, like deadlifting and reading about Mediterranean haplogroups
--
Wait, so, from fakest to realest IV>DD>RCT>RDD? That totally matches my impression.
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june 2017 by nhaliday
The Not-So-Hot Melting Pot: The Persistence of Outcomes for Descendants of the Age of Mass Migration
Large skill gaps across different immigrant sources may remain for generations; however, convergence past the second generation is typically unknown because data rarely include grandparent’s country of birth. I overcome this limitation with new historical data and show that skill differentials across European sources strongly persisted from the first generation in 1880 to the third generation in 1940. While skill gaps across source countries remained, immigrants’ descendants achieved highly relative to longer-established white Americans; by 1940, the third generation had surpassed those with four American-born grandparents on almost every economic measure.

- lol look at Figure 6 w/ Russia/Poland as an outlier on everything, wonder why that is...
- also Norwegians do pretty badly relative to almost everyone else (low in skilled and white collar), which reminds me of http://isteve.blogspot.com/2007/11/whos-who-in-science.html
"The higher achievement index of Swedes and Danes than of Norwegians is not a statistical aberration, but a reality. This is indicated by the magnitude of the difference and by the fact that Swedes lead Norwegians by significant numbers in the great majority of those rosters of achievement in which the comparison could be made."

other:
Long live your ancestors’ American dream: The self-selection and multigenerational mobility of American immigrants: https://drive.google.com/file/d/0B-cv6-rvQoj4eU5KUHRuMzZQY1k/view
Joakim Ruist: https://sites.google.com/site/joakimruist/
https://twitter.com/GarettJones/status/875491496085999616
Time to end pointless debates over the causes of the persistence and act like a time series econometrician: Believe it, start forecasting.
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march 2017 by nhaliday
Bounds on the Expectation of the Maximum of Samples from a Gaussian
σ/sqrt(pi log 2) sqrt(log n) <= E[Y] <= σ sqrt(2) sqrt(log n)

upper bound pf: Jensen's inequality+mgf+union bound+choose optimal t (Chernoff bound basically)
lower bound pf: more ad-hoc (and difficult)
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october 2016 by nhaliday
political analysis | West Hunter
Just to make things clear, most political reporters are morons, nearly as bad as sports reporters. Mostly ugly cheerleaders for their side, rather than analysts. Uninteresting.

how to analyze polls:

Who ever is ahead in the polls at the time of election is extremely likely to win. Talk about how Candidate X would have a ‘difficult path to 270 electoral votes’ when he’s up 2 points (for example), is pretty much horseshit. There are second-order considerations: you get more oomph per voter when the voter is in a small state, and you also want your votes distributed fairly evenly, so that you win states giving you a majority of electoral votes by a little rather than winning states giving you a minority of electoral votes by huge margins. Not that a candidate can do much about this, of course.

When you hear someone say that it’s really 50 state contests [ more if you think about Maine and Nebraska] , so you should pay attention to the state polls, not the national polls: also horseshit. In some sense, it is true – but when your national polls go up, so do your state polls – almost all of them, in practice. On election day, or just before, you want to consider national polls rather than state polls, because they are almost always more recent, therefore more accurate.

When should you trust an outlier poll, rather than the average: when you want to be wrong.

Money doesn’t help much. Political consultants will tell you that it does, but then they get 15% of ad buys.

A decent political reporter would actually go out and talk to people that aren’t exactly like him. Apparently this no longer happens.

All of these rules have exceptions – but if you understand those [rare] exceptions and can apply them, you’re paying too much attention to politics.
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september 2016 by nhaliday

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