Results for “small group theory” 43 found
Two all-purpose pieces of advice: small groups and mentors
That is the theme of my latest Bloomberg column, here is one excerpt:
The first piece of advice stems from what has been dubbed in Silicon Valley “the small group theory.” It goes like this:
- When working on any kind of problem, task or question, embed yourself in a small group of peers with broadly similar concerns.
And:
The second near-universal piece of advice is this:
- Get mentors.
Those two pieces of advice, unlike most advice, hold for a very broad variety of contexts. Do read the column, but here is some further detail:
Mentorship can be general or specialized. I have had classical-music mentors, art-market mentors, country-specific mentors when I lived in Germany and New Zealand, foreign-language mentors, chess mentors, economics mentors, philosophy mentors, writing mentors and friendly mentors to help with the basic emotional issues of life. I’ve tried to find mentors for just about everything. Sometimes the relationship lasts only a week or a month, other times for years.
Aside from providing teaching and advice, the mentor, like the small group, helps make an issue or idea more vivid: A living, breathing exemplar of success stands before you. The mentor makes a discipline feel more real and the prospect of success more realistic.
As a corollary, in addition to trying to find mentors, you should be willing to become a mentor yourself. Even if you do not have advanced understanding in some particular area, almost certainly there is someone who knows less than you do and who could use assistance. Being a mentor also helps you understand how to learn and appreciate your own mentors.
A mentor doesn’t have to be older than you, and in fact some of your mentors probably should be younger, especially since technologies are starting to change more rapidly. If you are 50 years old, the idea of an 18-year-old crypto mentor isn’t crazy. If the metaverse turns into a reality, don’t look to the graybeards for tutelage.
Recommended.
What are the best analyses of small, innovative, productive groups?
Shane emails me:
Hello!
What have you found to be the best books on small, innovative, productive groups?
These could be in-depth looks at specific groups – such as The Idea Factory, about Bell Labs – or they could be larger studies of institutions, guilds, etc.
I suggest reading about musical groups and sports teams and revolutions in the visual arts, as I have mentioned before, taking care you are familiar with and indeed care passionately about the underlying area in question. Navy Seals are another possible option for a topic area. In sociology there is network theory, but…I don’t know. In any case, the key is to pick an area you care about, and read in clusters, rather than hoping to find “the very best book.” The very theory of small groups predicts this is how you should read about small groups!
But if you must start somewhere, Randall Collins’s The Sociology of Philosophies is probably the most intensive and detailed place to start, too much for some in fact and arguably the book strains too hard at its target.
I have a few observations on what I call “small group theory”:
1. If you are seeking to understand a person you meet, or might be hiring, ask what was the dominant small group that shaped the thinking and ideas of that person, typically (but not always) at a young age. Step #1 is often “what kind of regional thinker is he/she?” and step #2 is this.
2. If you are seeking to foment change, take care to bring together people who have a relatively good chance of forming a small group together. Perhaps small groups of this kind are the fundamental units of social change, noting that often the small groups will be found within larger organizations. The returns to “person A meeting person B” arguably are underrated, and perhaps more philanthropy should be aimed toward this end.
3. Small groups (potentially) have the speed and power to learn from members and to iterate quickly and improve their ideas and base all of those processes upon trust. These groups also have low overhead and low communications overhead. Small groups also insulate their members sufficiently from a possibly stifling mainstream consensus, while the multiplicity of group members simultaneously boosts the chances of drawing in potential ideas and corrections from the broader social milieu.
4. The bizarre and the offensive have a chance to flourish in small groups. In a sense, the logic behind an “in joke” resembles the logic behind social change through small groups. The “in joke” creates something new, and the small group can create something additionally new and in a broader and socially more significant context, but based on the same logic as what is standing behind the in joke.
5. How large is a small group anyway? (How many people can “get” an inside joke?) Has the internet made “small groups” larger? Or possibly smaller? (If there are more common memes shared by a few thousand people, perhaps the small group needs to be organized around something truly exclusive and thus somewhat narrower than in times past?)
6. Can a spousal or spouse-like couple be such a small group? A family (Bach, Euler)?
7. What are the negative social externalities of such small groups, compared to alternative ways of generating and evaluating ideas? And how often in life should you attempt to switch your small groups?
8. What else should we be asking about small groups and the small groups theory of social change?
9. What does your small group have to say about this?
I thank an anonymous correspondent — who adheres to the small group theory — for contributions to this post.
A simple theory of Moore’s Law and social media
1. Moore’s Law plus the internet makes smart people smarter, and stupid people less smart.
2. Manipulable people can be reached with a greater flood of information, so over time as data on them accumulate, they become more manipulable.
3. It is often easier to manipulate smart people than stupid people, because the latter may be oblivious to a greater set of cues and clues.
4. Social media bring smarter people together with the less smart more than used to be the case, Twitter more so than Facebook. Members of each group are appalled by what they experience. The smarter people see the lesser smarts of many others. The less smart people — who often are not entirely so stupid after all — can see how manipulated the smarter people are. They also see that the smarter people look down on them and attack their motives and intellects. Both groups go away thinking less of each other.
4b. The smarter people, in reacting this way, in fact are being manipulated by the (stupider) powers that be.
5. “There is a performative dimension that renders both sides more rigid and dishonest.” From a correspondent.
6. Consider a second distinction, namely between people who are too sensitive to social information, and people who are relatively insensitive to social information. A quick test of this one is to ask how often a person’s tweets (and thoughts) refer to the motivations, intentions, or status hierarchies held by others. Get the picture? (Here is an A+ example.)
7. People who are overly sensitive to social information will be driven to distraction by Twitter. They will find the world to be intolerably bad. The status distinctions they value will be violated so, so many times, and in a manner which becomes common knowledge. And they will perceive what are at times the questionable motives held by others. Twitter is like negative catnip for them. In fact, they will find it more and more necessary to focus on negative social information, thereby exacerbating their own tendencies toward oversensitivity.
8. People who are not so sensitive to social information will pursue social media with greater equanimity, and they may find those media productivity-enhancing. Nevertheless they will become rather visibly introduced to a relatively new category of people for them — those who are overly sensitive to social information. This group will become so transparent, so in their face, and also somewhat annoying. Even those extremely insensitive to social information will not be able to help perceiving this alternate approach, and also the sometimes bad motivations that lie behind it. The overly sensitive ones in turn will notice that another group is under-sensitive to the social considerations they value. These two groups will think less and less of each other. The insensitive will have been made sensitive. It’s like playing “overrated vs. underrated” almost 24/7 on issues you really care about, and which affect your own personal status.
9. The philosophy of Stoicism will return to Silicon Valley. It will gain adherents but fail, because the rest of the system is stacked against it.
10. The socially sensitive, very smart people will become the most despairing, the most manipulated, and the most angry. The socially insensitive will either jump ship into the camp of the socially sensitive, or they will cultivate new methods of detachment, with or without Stoicism. Straussianism will compete with Stoicism.
11. Parts of social media will peel off into smaller, more private groups. At the end of the day, many will wonder which economies of scale and scope have been lost. And gained. Others will be too manipulated to wonder such things.
12. The “finance guy” in me thinks: how can I use all this for intellectual arbitrage? Which camp does that put me in?
13. What bounds this process?
*Jena 1800*
Jena had only about five thousand inhabitants at the time, but for a while in the early nineteenth century it was the center of German intellectual life. In one house (Leutragasse 5) you had living, at the same time, Friedrich and August Wilhelm Schlegel, Caroline Schlegel, Dorothea Veit, Friedrich Wilhelm Joseph Schelling, Friedrich von Hardenberg (“Novalis”), and Ludwig Tieck.
How is that for “small group theory“?
Fichte, Goethe, and Hegel show up as well!
The author of the book is Peter Neumann, and the subtitle is The Republic of Free Spirits. It is not captivating (Germanic style, then translated), but I found it valuable nonetheless. Which other contemporary work covers this remarkable assemblage of talent?
Wednesday assorted links
2. Composers fit “the small group theory.”
3. Mayda, Peri, and Steingress (AEA gate): “Our main contribution is to show that an increase in high-skilled immigrants decreases the share of Republican votes, while an inflow of low-skilled immigrants increases it.”
4. Paul Krugman on credibility, deficits, and inflation.
5. Half of the unvaccinated claim they wouldn’t take the Pfizer pill.
In praise of art books
Running out of things to read? Do you ever have the sneaky feeling that books might be overrated? Well, for some variation at the margin try reading art books. That’s right, books about art. Not “how to draw,” but books about the content and history of art. Some of them you might call art history, but that term makes me a little nervous. Just go into a good art museum, and look at what they are stocking in their bookstore. Many of them will be picture books, rather than art history in the narrower, more scholarly sense of that word.
Art books offer the following advantages:
1. They are among the best ways to learn history, politics, and yes science too (advances in art often followed advances in science and technology). Even economic history. Since the main focus is the art, they will give you “straight talk” about the historical period in question, rather than trying to organize the narrative around some vague novelty that only the peer reviewers care about.
2. They are often very pretty to look at. You also feel you can read them in small bites, or you can read only a single chapter or section. The compulsion to finish is relatively weak, a good thing. You can feel you have consumed them without reading them at all, a true liberation, which in turns means you will read them as you wish to.
3. They have passed through different filters than most other books, precisely because they are often “sold into the market” on the basis of their visuals, or copyright permissions, or connection with a museum exhibit, or whatever. Thus they introduce variation into your reading life, compared to say traditional academic tomes or “trade books,” which increasingly are about gender, race, and DT in an ever-more homogenized fashion.
4. They are among the best ways of learning about the sociology of creativity and also “the small group theory” of history.
5. These books tend not to be politically contentious, or if they are it is in a superficial way that is easily brushed off. (Note there is a whole subgenre of art books, from theory-laden, left-wing presses, with weird covers, displayed in small, funky Manhattan or Brooklyn bookstores where you can’t believe they can make the rent, where politics is all they are about. Avoid those.)
6. A bookstore of art books is almost always excellent, no matter how small. It’s not about comprehensiveness, rather you can always find numerous books there of interest.
7. Major reviewing outlets either do not cover too many art books, or they review them poorly and inaccurately. That suggests your “marginal best book” in the art books category is really quite good, because you didn’t have an easy means to discover it.
8. You might even wish to learn about art.
9. This whole genre is not about assembling a reading list of “the best art books.” Go to a good public library, or museum bookstore, and start grabbing titles. The best museum bookstore I know of is at the Metropolitan Museum of Art in New York.
10. It is also a very good introduction to the histories and cultures of locations such as China and India, where “straight up” political histories numb you with a succession of names, periods, and dynasties, only barely embedded in contexts that make any sense to you.
On white flight (from the comments)
Are whites fleeing from Asian-heavy California public schools? One recent paper suggested maybe so, but abc raises some doubts:
I don’t want to dismiss the paper out of hand, as I have seen time and again the challenges communities face both in and outside of the school setting in accommodating demographic change.
However, I don’t think the headline result in this paper is particularly credible. First, there isn’t a well-articulated research question to guide the choice of regression. Second, the authors implicitly rely on the “an instrument is always better” fallacy rather than explaining why their instrument yields more reliable estimates than naive OLS for the (unstated) question of interest. Taken together, the paper is undergrad-thesis level material elevated only by a click bait topic and result. If we want to make bold claims about White animosity towards Asians (a claim that also constructive of such animosity and counter-animosity from Asians towards Whites) we should demand substantive evidence. This paper does not present such evidence.
Some key takeaways:
(1) The authors note that a mechanical housing market replacement would suggest a one-for-one effect, but say that their -1.47 effect is above that threshold. However, if we check the confidence interval using a conservative 1.96 critical value and the estimated standard error of the coefficient estimate, we have -1.47 + 1.96*0.268 = -0.96 so that we are not statistically significantly different from -1 by this measure.
(2) The naive OLS estimate in high-SES regions is -0.6, well below the fixed enrollment effect of -1. The authors speculate that OLS may be biased downward because the error term include unmeasured district quality changes that draw in both Asians and Whites. (Note such a correlation only operates if enrollment is not capped, so inconsistent with that model.) The authors don’t document any of these omitted variable issues, however, and just assert that their instrument will be better.
(3) Authors do not substantively engage issues with their IV. First, the IV doesn’t account for changes in composition of immigrants over time (increasing wealth and education of Asian arrivals relative to earlier waves) nor does it account for movement of second-generation Asian families. If there is no omitted variable bias but the instrumented entry is lower than the actual entry, then mechanically the coefficient will have to be higher to offset this effect and restore least-squares minimization.
(4) The instrumented Asian inflows coefficient could pick up effects from Asian-agglomeration effects. A one unit increase in Asian enrollment from pure fixed-pattern immigration flows made lead to shifts of previously settled Asians or shift the direction of subsequent immigration. For example, a settled Korean in Riverside who sees large increases in Korean population in Orange County may see OC as being more attractive than before and move into the area. This induced shift may be only partially captured by the first-stage prediction, leaving the 2nd stage coefficient of interest to increase in magnitude.
(5) Various sensitivities lead to surprising results. First, the instrument behaves poorly in some subsamples, e.g. the bottom-half of the SES scale. Why should we believe an instrument in one data subset when it plainly fails in the complement? Second, the instrument is insignificant in the Bottom Tercile of the above-median SES group (appendix table 2). Third, the IV estimate is only -0.841 in the top tercile of the above-median SES group, again below the key -1 threshold if enrollment caps are binding. Taken together, are we to think that we can identify white flight using this instrument only for the 66.6th to 83.3th percentile bucket?
(6) There’s just a big background trend issue that one has to worry about here. The theory of white flight begs the question of “flight to where?” However if we just look at Appendix Figure 2 during this time period there is a big drop in total White enrollment (and a small decline in Black enrollment) while Asian and Hispanic enrollment see big increases. To what extent are we just finding that aging out of whites in high-SES regions is being replaced disproportionately by Asians?
(7) A couple other wrinkles: how are mixed-race students handled? how would demographic shifts in total enrollment by district affect the 1-to-1 threshold? If child population is shrinking over time (e.g. because families are leaving CA, children per family is declining) then normal churn would predict more than 1-to-1 replacement of new-cohort race versus previous-cohort race.
So perhaps the right answer is “no”?
The Story of VaccinateCA
The excellent Patrick McKenzie tells the story of VaccineCA, the ragtag group of volunteers that quickly became Google’s and then the US Government’s best source on where to find vaccines during the pandemic.
Wait. The US Government was giving out the vaccines. How could they not know where the vaccines were? It’s complicated. Operation Warp Speed delivered the vaccines to the pharmacy programs and to the states but after that they dissappeared into a morass of incompatible systems.
[L]et’s oversimplify: Vials were allocated by the federal government to states, which allocated them to counties, which allocated them to healthcare providers and community groups. The allocators of vials within each supply chain had sharply limited ability to see true systemic supply levels. The recipients of the vials in many cases had limited organizational ability to communicate to potential patients that they actually had them available.
Patients then asked the federal government, states, counties, healthcare providers and community groups, ‘Do you have the vaccine?’ And in most cases the only answer available to the person who picked up the phone was ‘I don’t have it. I don’t know if we have it. Plausibly someone has it. Maybe you should call someone else.’ Technologists will see the analogy to a distributed denial of service incident, and as if the overwhelming demand was not enough of a problem, the rerouting of calls between institutions amplified the burden on the healthcare system. Vaccine seekers were routinely making dozens of calls.
This caused a standing wave of inquiries to hit all levels of US healthcare infrastructure in the early months of the vaccination effort. Very few of those inquiries went well for any party. It is widely believed, and was widely believed at the time, that this was primarily because supply was lacking, but it was often the case that supply was frequently not being used as quickly as it was produced because demand could not find it.
It turned out that the best way to get visibility into this mess was not to trace the vaccines but to call the endpoints on the phone and then create a database that people could access which is what VaccinateCA did but in addition to finding the doses they had to deal with the issue of who was allowed access.
A key consideration for us, from the first day of the effort, was recording not just which pharmacist had vials but who they thought they could provide care to. This was dependent on prevailing regulations in their state and county, interpretations of those regulations by the pharmacy chain, and (frequently!) ad hoc decision-making by individual medical providers. Individual providers routinely made decisions that the relevant policy makers did not agree comported with their understanding of the rules.
VaccinateCA saw the policy sausage made in real time in California while keeping an eye on it nationwide. It continues to give me nightmares.
California, not to mince words, prioritized the appearance of equity over saving lives, over and over and over again, as part of an explicitly documented strategy, at all levels of the government. You can read the sanitized version of the rationale, by putative medical ethics experts, in numerous official documents. The less sanitized version came out frequently in meetings.
This was the official strategy.
The unofficial strategy, the result the system actually obtained, was that early access to the vaccine was preferentially awarded based on proximity to power and to the professional-managerial class.
… The essential workers list heavily informed the vaccination prioritization schedule. Lobbyists used it as procedural leverage to prioritize their clients for vaccines. The veterinary lobby was unusually candid, in writing, about how it achieved maximum priority (1A) for veterinarians due to them being ‘healthcare workers’.
Teachers’ unions worked tirelessly and landed teachers a 1B. They were ahead of 1C, which included (among others) non-elderly people for whom preexisting severe disability meant that ‘a covid-19 infection is likely to result in severe life-threatening illness or death’. The public rationale was that teachers were at elevated risk of exposure through their occupation. Schools were, of course, mostly closed at the time, and teachers were Zooming along with the rest of the professional-managerial class, but teachers’ unions have power and so 1B it was. Young, healthy teachers quarantining at home were offered the vaccine before people who doctors thought would probably die if they caught Covid.
Now repeat this exercise up and down the social structure and economy of the United States.
…Healthcare providers were fired for administering doses that were destined to expire uselessly. The public health sector devoted substantial attention to the problem of vaccinating too many people during a pandemic. Administration of the formal spoils system became farcically complicated and frequently outcompeted administration of the vaccine as a goal.
The process of registering for the vaccine inherited the complexity of the negotiation over the prioritization, and so vulnerable people were asked to parse rules that routinely befuddled healthy professional software engineers and healthcare administrators – the state of New York subjected senior citizens to a ‘51 step online questionnaire that include[d] uploading multiple attachments’!
That isn’t hyperbole! New York meant to do that! On purpose!
Lives were sacrificed by the thousands and tens of thousands for political reasons. Many more were lost because institutions failed to execute with the competence and vigor the United States is abundantly capable of.
…The State of California instituted a policy of redlining in the provision of medical care in a pandemic to thunderous applause from its activist class and medical ethics experts….Residency restrictions were pervasively enforced at the county level and frequently finer-grained than that. A pop-up clinic, for example, might have been restricted to residents of a single zip code or small group of zip codes.
All people are equal in the eyes of the law in California, but some people are . . . let’s politely say ‘administratively disfavored’.
The theory was, and you could write down this part of it, disfavored potential patients might use social advantages like better access to information and transportation to present themselves for treatment at locations that had doses allocated for favored potential patients. This part of the theory was extremely well-founded. Many people were willing to drive the length and breadth of California for their dose and did so.
What many wanted to do, and this is the part that they couldn’t write down, is deny healthcare to disfavored patients. Since healthcare providers are public accommodations in the state of California, they are legally forbidden from discriminating on the basis of characteristics that some people wanted to discriminate on. So that was laundered through residency restrictions.
Many more items of interest. I didn’t know this incredibly fact about the Biden adminsitratins Vaccines.gov for example:
Pharmacies through the FRPP had roughly half of the doses; states and counties had roughly the other half (sometimes administered at pharmacies, because clearly this isn’t complicated enough yet). You would hope that state and county doses were findable on Vaccines.gov. It was going to be the centerpiece of the Biden administration’s effort to fix the vaccine finding problem and take credit for doing so.
…Since the optics would be terrible if America appeared to serve some states much better than others on the official website that everyone would assume must show all the doses, no state doses, not even from states that would opt in, would be shown on it, at least not at the moment of maximum publicity. Got that?
A good point about America.
We also benefited from another major strength of America: You cannot get arrested, jailed, or shot for publishing true facts, even if those facts happen to embarrass people in positions of power. Many funders wanted us to expand the model to a particular nation. In early talks with contacts there in civil society, it was explained repeatedly and at length that a local team that embarrassed the government’s vaccination rollout would be arrested and beaten by people carrying guns. This made it ethically challenging to take charitable donations and try to recruit that team.
Many more points of interest about the process of running a medical startup during a pandemic. Read the whole thing.
Where I differ from Bryan Caplan’s *Labor Econ Versus the World*
One thing I liked about reading this book is I was able to narrow down my disagreements with Bryan to a smaller number of dimensions. And to be clear, I agree with a great deal of what is in this book, but that does not make for an interesting blog post. So let’s focus on where we differ. One point of disagreement surfaces when Bryan writes:
Tenet #6: Racial and gender discrimination remains a serious problem, and without government regulation, would still be rampant.
Critique: Unless government requires discrimination, market forces make it a marginal issue at most. Large group differences persist because groups differ largely in productivity.
I would instead stress that most of the inequity occurs upstream of labor markets, through the medium of culture. It is simply much harder to be born in the ghetto! I am fine with not calling this “discrimination,” and indeed I do not myself use the word that way. Still, it is a significant inequity, and it is at least an important a lesson about labor markets as what Bryan presents to you.
But you won’t find much consideration of it in Bryan’s book. The real problems in labor markets arise when “the cultural upstream” intersects with other social institutions in problematic ways. To give a simple example, Princeton kept Jews out for a long time, and that was not because of the government. Or Princeton voted to admit women only in 1969, again not the government. What about Major League Baseball before Jackie Robinson or even for a long while after? Much of Jim Crow was governmental, but so much of it wasn’t. There are many such examples, and I don’t see that Bryan deals with them. And they have materially affected both people’s lives and their labor market histories, covering many millions of lives, arguably billions.
Or, the Indian government takes some steps to remedy caste inequalities, but fundamentally the caste system remains, for whatever reasons. Again, this kind of cultural upstream isn’t much on Bryan’s radar screen. (I have another theory that this neglect of culture is because of Bryan’s unusual theory of free will, through which moral blame has to be assigned to individual choosers, but that will have to wait for another day!)
We can go beyond the discrimination topic and still see that Bryan is not paying enough attention to what is upstream of labor markets, or to how culture shapes human decisions.
Bryan for instance advocates open borders (for all countries?). I think that would be cultural and political suicide, most of all for smaller countries, but for the United States too. You would get fascism first, if anything. I do however favor boosting (pre-Covid) immigration flows into the United States by something like 3x. So in the broader scheme of things I am very pro-immigration. I just think there are cultural limits to what a polity can absorb at what speed.
If you consider Bryan on education, he believes most of higher education is signaling. In contrast, I see higher education as giving its recipients the proper cultural background to participate in labor markets at higher productivity levels. I once wrote an extensive blog post on this. That is how higher education can be productive, while most of your classes seem like a waste of time.
On poverty, Bryan puts forward a formula of a) finish high school, b) get a full time job, and c) get married before you have children. All good advice! But I find that to be nearly tautologous as an explanation of poverty. To me, the deeper and more important is why so many cultures have evolved to make those apparent “no brainer” choices so difficult for so many individuals. Again, I think Bryan is neglecting the cultural factors upstream of labor markets and in this case also marriage markets. One simple question is why some cultures don’t produce enough men worth marrying, but that is hardly the only issue on the table here.
More generally, I believe that once you incorporate these messy “cultural upstream” issues, much of labor economics becomes more complicated than Bryan wishes to acknowledge. Much more complicated.
I should stress that Bryan’s book is nonetheless a very good way to learn economic reasoning, and a wonderful tonic against a lot of the self-righteous, thoughtless mood affiliation you will see on labor markets, even coming from professional economists.
I will remind that you can buy Bryan’s book here, and at a very favorable price point.
Yglesias on CRT
Matt Yglesias has an excellent post on schooling and politics emphasizing three points. First, there is a lot of diversity, equity, inclusion (DEI) nonsense which the schools are using to train teachers and administrators. Second, at the same time the school administrators/teacher’s unions are generally ignoring the very real cost to children and parents of the school closures, including the costs of a widening racial gap. Third, the schools are stigmatizing testing under the guise of promoting equity but in reality because the teacher’s unions know that when you test children you learn that not all teachers are equally capable.
[The DC Public Schools] also recommend that people read a bunch of Robin DiAngelo books and brag that “more than 2,000 DCPS staff have participated in Courageous Conversation training.” But is Courageous Conversation training a good idea? This NYT Magazine profile of the company and its founder made it sound pretty bad:
Singleton, who holds degrees from the University of Pennsylvania and Stanford, and who did stints in advertising and college admissions before founding what’s now known as Courageous Conversation in 1992, talks about white culture in similar ways. There is the myth of meritocracy. And valuing “written communication over other forms,” he told me, is “a hallmark of whiteness,” which leads to the denigration of Black children in school. Another “hallmark” is “scientific, linear thinking. Cause and effect.” He said, “There’s this whole group of people who are named the scientists. That’s where you get into this whole idea that if it’s not codified in scientific thought that it can’t be valid.” He spoke about how the ancient Egyptians had “ideas about how humanity works that never had that scientific-hypothesis construction” and so aren’t recognized. “This is a good way of dismissing people. And this,” he continued, shifting forward thousands of years, “is one of the challenges in the diversity-equity-inclusion space; folks keep asking for data. How do you quantify, in a way that is scientific — numbers and that kind of thing — what people feel when they’re feeling marginalized?” For Singleton, society’s primary intellectual values are bound up with this marginalization.
I don’t think Frankfurt School Marxists are going to take over society by injecting these ideas into K-12 schools or anything like that. What I so think is that time and money is being wasted on initiatives that are run by people who are somewhere between stupid and fraudulent.
And it’s important to take that seriously, not just because someone somewhere may take these goofy ideas seriously (see prior commentary about Tema Okun), but because fiscal tradeoffs are real. Dollars spent on DEI trainings that come with zero proof of efficacy are dollars that can’t be invested in things like D.C.’s successful teacher bonus pay program, updating school air conditioning, improving school lunches, reducing kids’ exposure to air pollution and lead poisoning, or any of the other various interventions that have decent evidence behind them.
Of course when I say that investing in higher quality school lunches is good for kids’ learning, what I mean is that it’s good as measured on standardized tests.
Standardized testing has become a weird discourse flashpoint, but I think everyone agrees that you can, in principle, assess someone’s competence in a given subject area with a test. And if you want to compare different people, you need to give them the same test. It’s only by making comparisons across classrooms and across time that we are able to persuasively demonstrate that particulates are bad for school performance, healthy meals are good for school performance, and air conditioning improves school performance in the summer.
All this would be uncontroversial, I think, except teachers’ unions don’t like the idea of assessing teachers based on their job performance.
Read the whole thing and subscribe to Slow Boring.
The relevance of ZMP and near-ZMP workers
From the St. Louis Fed:
Based on patterns of employment transitions, we identify three different types of workers in the US labor market: α’s β’s and γ’s. Workers of type α make up over half of all workers, are most likely to remain on the same job for more than 2 years and, when they become unemployed, typically find a new job within 1 quarter. Workers of type γ comprise less than one-fifth of workers, have a low probability of staying on the same job for more than 2 years and, when they become unemployed, face a high probability of remaining jobless for more than 1 year. Workers of type β are in between αs and γ’s. The earnings losses caused by displacement are relatively small and transitory for α-workers, while they are large and persistent for γ-workers. During the Great Recession, excess unemployment for α-workers rose by little and was reabsorbed quickly; unemployment for γ-workers rose by 20 percentage points and was not reabsorbed 4 years after its peak. We use a search-theoretic model of the labor market to rationalize the different patterns of employment transitions across types. The model naturally explains both the variation in the consequences of job displacement across types, and the variation in the dynamics of unemployment during the Great Recession. Our view is that several puzzling micro and macro phenomena about the labor market are driven by the behavior of the small group of γ-workers.
Here is the NBER link. The authors are Victoria Gregory, Guido Menzio, and David Wiczer. The ZMP concept remains maligned and misrepresented, sometimes caricatured as a one-blade theory or as demand denialism, so I am happy to see this new evidence capturing the original intuition.
Via David Sinsky.
What I’ve been reading
1. Susan Bernofsky, Clairvoyant of the Small: The Life of Robert Walser. I believe you need to have read Walser first, but if so this is a far better biography than what you might have expected the English-speaking world to have produced. It is also an implicit portrait of where pre-WWI Europe went wrong, the history of micro-writing, and a paean to general weirdness, noting that Walser in both his life and writing is inexplicable to this day.
2. Andy Grundberg, How Photography Became Contemporary Art. How does a whole genre rise from also-ran status to a major (the major?) form of contemporary art? This is an excellent history with nice color plates and it is also a causal account. I liked this sentence, among others: “Surprisingly, the acceptance of color photography had happened earlier in the art world than in the so-called art photography world.” Polaroid had a significant role as well.
3. Colin Bryar and Bill Carr, Working Backwards: Insights, Stories, and Secrets from Amazon. A truly good and very substantive management book (I hear your jaw hitting the floor). Just that statement makes it one of the best management books ever. Really.
4. Tom Jones, George Berkeley: A Philosophical Life. A thorough biography of an 18th century Irish philosopher who is still worth reading. Berkeley also wrote on monetary theory and pioneered the idea of an abstract unit of account.
5. Ryan Bourne, Economics in One Virus: An Introduction to Economic Reasoning through Covid-19. This book came out yesterday, I read it earlier, and here is my blurb: “A truly excellent book that explains where our pandemic response went wrong, and how we can understand those failings using the tools of economics.” It is published by Cato, a libertarian think tank, and it is a much better and more integrated and science-based account than what you might find from other groups, whether libertarian or non-libertarian.
How should you feel if you attentively finish Benjamin Storey and Jenna Silber Storey, Why We Are Restless: On the Modern Quest for Contentment?
Cameron Blevis, Paper Trails: The US Post and the Making of the American West, is a good book and on a more important topic than you might think.
The South Korean minimum wage hike
A controversial study on the effect of a radical rise in the legal minimum wage level came out Tuesday, pitting employers against employees in the midst of negotiations for the next year’s wage standard.
Researchers at the Korea Economic Research Institute analyzed in the study the impact of the 16.4 percent increase in the 2018 wage level on low-income workers to find that many low-paying jobs were erased, while those who were employed enjoyed higher pay. The institute is affiliated with the country’s top business lobby, the Federation of Korean Industries.
The minimum wage is updated on an annual basis, and the rate currently stands at 8,590 won ($7.10) per hour. In 2018, the rate rose 16.4 percent from 6,470 won a year earlier to 7,530 won, the steepest increase in 17 years.
The KERI report said the employment rate in 2018 for workers directed affected by the hike — those who were getting paid less than the 2018 legal wage in 2017 — was as much as 4.6 percentage points lower than other income groups.
Some 15.1 percent of this group were jobless in 2018.
The study calculated that between 27.4 percent and 30.5 percent of the unemployment cases were due to the higher wage level, which prompted employers to cut jobs.
Here is the article. I cannot find this study, it may well only be in Korean (addendum: here is the link in Korean), and I note it is connected to a business lobby. Still, I will take this opportunity to ask: what else do we know about the recent and radical South Korean wage hike?
Here are some general remarks at Wikipedia. And here is a relevant paper about minimum wage hikes in Hungary: small disemployment effects after four years, and most of the burden carried by consumers, which implies the monopsony model does not apply — in that model prices should fall!
And do read Brian Albrecht on the minimum wage.
Does Demand for New Currencies Increase in a Recession?
Every time there is a recession we hear more about barter and new currencies, especially so-called “local” currencies. An inceased interest in barter and new currencies suggests a theory of recessions, the lack of liquidity theory:
Bloomberg: “In times of crisis like the one we are jumping into, the main issue is lack of liquidity, even when there is work to be done, people to do it, and demand for it,” says Paolo Dini, an associate professorial research fellow at the London School of Economics and one of the world’s foremost experts on complementary currencies. “It’s often a cash flow problem. Therefore, any device or instrument that saves liquidity helps.”
I wrote about this several years ago but on closer inspection it’s not obvious that interest in barter or new currencies increases much in a recession or that these new currencies are helpful. Here’s my previous post (with a new graph) and no indent.
Nick Rowe explains that the essence of New Keynesian/Monetarist theories of recessions is the excess demand for money (Paul Krugman’s classic babysitting coop story has the same lesson). Here’s Rowe:
The unemployed hairdresser wants her nails done. The unemployed manicurist wants a massage. The unemployed masseuse wants a haircut. If a 3-way barter deal were easy to arrange, they would do it, and would not be unemployed. There is a mutually advantageous exchange that is not happening. Keynesian unemployment assumes a short-run equilibrium with haircuts, massages, and manicures lying on the sidewalk going to waste. Why don’t they pick them up? It’s not that the unemployed don’t know where to buy what they want to buy.
If barter were easy, this couldn’t happen. All three would agree to the mutually-improving 3-way barter deal. Even sticky prices couldn’t stop this happening. If all three women have set their prices 10% too high, their relative prices are still exactly right for the barter deal. Each sells her overpriced services in exchange for the other’s overpriced services….
The unemployed hairdresser is more than willing to give up her labour in exchange for a manicure, at the set prices, but is not willing to give up her money in exchange for a manicure. Same for the other two unemployed women. That’s why they are unemployed. They won’t spend their money.
Keynesian unemployment makes sense in a monetary exchange economy…it makes no sense whatsoever in a barter economy, or where money is inessential.
Rowe’s explanation put me in mind of a test. Barter is a solution to Keynesian unemployment but not to “RBC unemployment” which, since it is based on real factors, would also occur in a barter economy. So does barter increase during recessions?
There was a huge increase in barter and exchange associations during the Great Depression with hundreds of spontaneously formed groups across the country such as California’s Unemployed Exchange Association (U.X.A.). These barter groups covered perhaps as many as a million workers at their peak.
In addition, I include with barter the growth of alternative currencies or local currencies such as Ithaca Hours or LETS systems. The monetization of non-traditional assets can alleviate demand shocks which is one reason why it’s good to have flexibility in the definition of and free entry into the field of money (a theme taken up by Cowen and Kroszner in Explorations in New Monetary Economics and also in the free banking literature.)
During the Great Depression there was a marked increase in alternative currencies or scrip, now called depression scrip. In fact, Irving Fisher wrote a now forgotten book called Stamp Scrip. Consider this passage and note how similar it is to Nick’s explanation:
If proof were needed that overproduction is not the cause of the depression, barter is the proof – or some of the proof. It shows goods not over-produced but dead-locked for want of a circulating transfer-belt called “money.”
Many a dealer sits down in puzzled exasperation, as he sees about him a market wanting his goods, and well stocked with other goods which he wants and with able-bodied and willing workers, but without work and therefore without buying power. Says A, “I could use some of B’s goods; but I have no cash to pay for them until someone with cash walks in here!” Says B, “I could buy some of C’s goods, but I’ve no cash to do it with till someone with cash walks in here.” Says the job hunter, “I’d gladly take my wages in trade if I could work them out with A and B and C who among them sell the entire range of what my family must eat and wear and burn for fuel – but neither A nor B nor C has need of me – much less could the three of them divide me up.” Then D comes on the scene, and says, “I could use that man! – if he’d really take his pay in trade; but he says he can’t play a trombone and that’s all I’ve got for him.”
“Very well,” cries Chic or Marie, “A’s boy is looking for a trombone and that solves the whole problem, and solves it without the use of a dollar.
In the real life of the twentieth century, the handicaps to barter on a large scale are practically insurmountable….
Therefore Chic or somebody organizes an Exchange Association… in the real life of this depression, and culminating apparently in 1933, precisely what I have just described has been taking place.
What about today (2011)? Unfortunately, the IRS doesn’t keep statistics on barter (although barterers are supposed to report the value of barter exchanges). Google Trends shows an increase in searches for barter in 2008-2009 but the increase is small. Some reports say that barter is up but these are isolated (see also the 2020 Bloomberg piece), I don’t see the systematic increase we saw during the Great Depression. I find this somewhat surprising as the internet and barter algorithms have made barter easier.
In terms of alternative currencies, the best data that I can find shows that the growth of alternative currencies in the United States is small, sporadic and not obviously increasing with the recession. (Alternative currencies are better known in Germany and Argentina perhaps because of the lingering influence of Heinrich Rittershausen and Silvio Gesell).
Below is a similar graph for 2017-2020. Again not much increase in recent times.
In sum, the increase in barter and scrip during the Great Depression is supportive of the excess demand for cash explanation of that recession, even if these movements didn’t grow large enough, fast enough to solve the Great Depression. Today there seems to be less interest in barter and alternative currencies than expected, or at least than I expected, given an AD shock and the size of this recession. I don’t draw strong conclusions from this but look forward to further research on unemployment, recessions and barter.
More on economists and epidemiologists
From my email box, here are perspectives from people in the world of epidemiology, the first being from Jacob Oppenheim:
I’d note that epidemiology is the field that has most embraced novel and principles-driven approaches to causal inference (eg those of Judea Pearl etc). Pearl’s cluster is at UCLA; there’s one at Berkeley, and another at Harvard.
The one at Harvard simultaneously developed causal methodologies in the ’70s (eg around Rubin), then a parallel approach to Pearl in the ’80s (James Robins and others), leading to a large collection of important epi people at HSPH (Miguel Hernan, etc). Many of these methods are barely touched in economics, which is unfortunate given their power in causal inference in medicine, disease, and environmental health.
These methods and scientists are very influential not only in public health / traditional epi, but throughout the biopharma and machine learning worlds. Certainly, in my day job running data science + ml in biotech, many of us would consider well trained epidemiologists from these top schools among the best in the world for quantitative modeling, especially where causality is involved.
From Julien SL:
I’m not an epidemiologist per se, but I think my background gives me some inputs into that discussion. I have a master in Mechatronics/Robotics Engineering, a master in Management Science, and an MBA. However, in the last ten years, epidemiology (and epidemiology forecasting) has figured heavily in my work as a consultant for the pharma industry.
[some data on most of epidemiology not being about pandemic forecasting]…
The result of the neglect of pandemics epidemiology is that there is precious little expertise in pandemics forecasting and prevention. The FIR model (and it’s variants) that we see a lot these days is a good teaching aid. Still, it’s not practically useful: you can’t fit exponentials with unstable or noisy parameters and expect good predictions. The only way to use R0 is qualitatively. When I saw the first R0 and mortality estimates back in January, I thought “this is going to be bad,” then sold my liquid assets, bought gold, and naked puts on indices. I confess that I didn’t expect it to be quite as bad as what actually happened, or I would have bought more put options.
…here are a few tentative answers about your “rude questions:”
a. As a class of scientists, how much are epidemiologists paid? Is good or bad news better for their salaries?
Glassdoor data show that epidemiologists in the US are paid $63,911 on average. CDC and FDA both pay better ($98k and $120k), as well as pharma (Merck: $94k-$115k). As explained above, most are working on cancer, diabetes, etc. So I’m not sure what “bad news” would be for them.
b. How smart are they? What are their average GRE scores?
I’m not sure where you could get data to answer that question. I know that in pharma, many – maybe most – people who work on epidemiology forecasting don’t have an epidemiology degree. They can have any type of STEM degree, including engineering, economics, etc. So my base rate answer would be average of all STEM GRE scores. [TC: Here are U. Maryland stats for public health students.]
c. Are they hired into thick, liquid academic and institutional markets? And how meritocratic are those markets?
Compared to who? Epidemiology is a smaller community than economics, so you should find less liquidity. Pharma companies are heavily clustered into few geographies (New Jersey, Basel in Switzerland, Cambridge in the UK, etc.) so private-sector jobs aren’t an option for many epidemiologists.
d. What is their overall track record on predictions, whether before or during this crisis?
CDC has been running flu forecasting challenges every year for years. From what I’ve seen, the models perform reasonably well. It should be noted that those models would seem very familiar to an econometric forecaster: the same time series tools are used in both disciplines. [TC: to be clear, I meant prediction of new pandemics and how they unfold]
e. On average, what is the political orientation of epidemiologists? And compared to other academics? Which social welfare function do they use when they make non-trivial recommendations?
Hard to say. Academics lean left, but medical doctors and other healthcare professionals often lean right. There is a conservative bias to medicine, maybe due to the “primo, non nocere” imperative. We see that bias at play in the hydroxychloroquine debate. Most health authorities are reluctant to push – or even allow – a treatment option before they see overwhelming positive proof, even when the emergency should encourage faster decision making.
…g. How well do they understand how to model uncertainty of forecasts, relative to say what a top econometrician would know?
As I mentioned above, forecasting is far from the main focus of epidemiology. However, epidemiologists as a whole don’t seem to be bad statisticians. Judea Pearl has been saying for years that epidemiologists are ahead of econometricians, at least when it comes to applying his own Structural Causal Model framework… (Oldish) link: http://causality.cs.ucla.edu/blog/index.php/2014/10/27/are-economists-smarter-than-epidemiologists-comments-on-imbenss-recent-paper/
I’ve seen a similar pattern with the adoption of agent-based models (common in epidemiology, marginal in economics). Maybe epidemiologists are faster to take up new tools than economists (which maybe also give a hint about point e?)
h. Are there “zombie epidemiologists” in the manner that Paul Krugman charges there are “zombie economists”? If so, what do you have to do to earn that designation? And are the zombies sometimes right, or right on some issues? How meta-rational are those who allege zombie-ism?
I don’t think so. Epidemiology seems less political than economy. There are no equivalents to Smith, Karl Marx, Hayek, etc.
i. How many of them have studied Philip Tetlock’s work on forecasting?
Probably not many, given that their focus isn’t forecasting. Conversely, I don’t think that Tetlock has paid much attention to epidemiology. On the Good Judgement website, healthcare questions of any type are very rare.
And here is Ruben Conner:
Weighing in on your recent questions about epidemiologists. I did my undergraduate in Economics and then went on for my Masters in Public Health (both at University of Washington). I worked as an epidemiologist for Doctors Without Borders and now work as a consultant at the World Bank (a place mostly run by economists). I’ve had a chance to move between the worlds and I see a few key differences between economists and epidemiologists:
-
Trust in data: Like the previous poster said, epidemiologists recognize that “data is limited and often inaccurate.” This is really drilled into the epidemiologist training – initial data collection can have various problems and surveys are not always representative of the whole population. Epidemiologists worry about genuine errors in the underlying data. Economists seem to think more about model bias.
-
Focus on implementation: Epidemiologists expect to be part of the response and to deal with organizing data as it comes in. This isn’t a glamorous process. In addition, the government response can be well executed or poorly run and epidemiologists like to be involved in these details of planning. The knowledge here is practical and hands-on. (Epidemiologists probably could do with more training on organizational management, they’re not always great at this.)
-
Belief in models: Epidemiologists tend to be skeptical of fancy models. This could be because they have less advanced quantitative training. But it could also be because they don’t have total faith in the underlying data (as noted above) and therefore see fancy specifications as more likely to obscure the truth than reveal it. Economists often seem to want to fit the data to a particular theory – my impression is that they like thinking in the abstract and applying known theories to their observations.
As with most fields, I think both sides have something to learn from each other! There will be a need to work together as we weigh the economic impacts of suppression strategies. This is particularly crucial in low-income places like India, where the disease suppression strategies will be tremendously costly for people’s daily existence and ability to earn a living.
Here is a 2014 blog post on earlier spats between economists and epidemiologists. Here is more from Joseph on that topic.
And here is from an email from epidemiologist Dylan Green:
So with that…on to the modelers! I’ll merely point out a few important details on modeling which I haven’t seen in response to you yet. First, the urgency with which policy makers are asking for information is tremendous. I’ve been asked to generate modeling results in a matter of weeks (in a disease which I/we know very little about) which I previously would have done over the course of several months, with structured input and validation from collaborators on a disease I have studied for a decade. This ultimately leads to simpler rather than more complicated efforts, as well as difficult decisions in assumptions and parameterization. We do not have the luxury of waiting for better information or improvements in design, even if it takes a matter of days.
Another complicated detail is the publicity of COVID-19 projections. In other arenas (HIV, TB, malaria) model results are generated all the time, from hundreds of research groups, and probably <1% of the population will ever see these figures. Modeling and governance of models of these diseases is advanced. There are well organized consortia who regularly meet to present and compare findings, critically appraise methods, elegantly present uncertainty, and have deep insights into policy implications. In HIV for example, models are routinely parameterized to predict policy impact, and are ex-post validated against empirical findings to determine the best performing models. None of this is currently in scope for COVID-19 (unfortunately), as policy makers often want a single number, not a range, and they want it immediately.
I hope for all of our sakes we will see the modeling coordination efforts in COVID-19 improve. And I ask my fellow epidemiologists to stay humble during this pandemic. For those with little specialty in communicable disease, it is okay to say “this isn’t my area of expertise and I don’t have the answers”. I think there has been too much hubris in the “I-told-ya-so” from people who “said this would happen”, or in knowing the obvious optimal policy. This disease continues to surprise us, and we are learning every day. We must be careful in how we communicate our certainty to policy makers and the public, lest we lose their trust when we are inevitably wrong. I suspect this is something that economists can likely teach us from experience.
One British epidemiologist wrote me and told me they are basically all socialists in the literal sense of the term. not just leaning to the left.
Another person in the area wrote me this:
Another issue that isn’t spoken about a lot is most Epidemiologists are funded by soft money. It makes them terrifyingly hard working but it also makes them worried about making enemies. Every critic now will be reviewed by someone in IHME at some point in an NIH study section, whereas IHME, funded by the Gates Foundation, has a lot of resilience. It makes for a very muted culture of criticism.Ironically, outsiders (like economist Noah Haber) trying to push up the methods are more likely to be attacked because they are not a part of the constant funding cycle.I wonder if economists have ever looked at the potential perverse incentives of being fully grant funded on academic criticism?
Here is an earlier email response I reproduced, here is my original blog post, here is my update from yesterday.