Inequality and social change, 2022 pontification edition

Someone paid me to talk about social trends for an hour. To get your month’s worth, I recorded my end of the conversation, cut out some of the dumber parts, and then tried out Happy Scribe to transcribe it, which cost a few dollars. The lightly edited text is below.

And you can listen to it on your holiday drive or doing the dishes. Sped up a little (I sound smarter and less boring that way) and with some editing, it’s 30 minutes. Here’s the Soundcloud link:

Here’s the text:

Fast and slow, unequally

I think my two overarching things are one, sort of a disjuncture between fast and slow. A lot of things have slowed down, but things have slowed down very unequally. So you have relatively rich people staying home all day while life goes by at the same speed on their phones, and at their jobs. And I think that just widens the gap in perceptions of how people see and understand the world.

And the second thing is really widening inequality. Inequality is very foundational to what’s happening socially right now. Even if you’re only looking at one person, the inequality affects that person because it affects their social context. So inequality is a property of groups but it affects everybody’s experience. This feeds into all kinds of other polarization that we have, just growing differences in perception and experience, which are increasingly sort of unstable or unpredictable.

In the olden days, when it took six months to get information from between Europe and North America, things that happened six months ago were only happening now. And then instant communication means the whole world is happening at the same time. That’s a very new experience for us. So time is perception of time and place is foundational. We have to get used to the size of what just happened. If we had something like a 40% drop in people moving around last spring – nothing like that has happened in modern times. So even if we get down to just 5% or 10%, that would have been huge on the previous scale. So even if there’s a large reduction in the pandemic effect, we’re still dealing with disruptions on a historic scale, even if things moderate quite a bit. I think we’re still looking at a quite different landscape when it comes to things like how people relate to their work, their physical spaces and other things, also, as far as sense of risk.

A lot of it depends on the pandemic. Some things are already certain — global travel is going to be disrupted. If all you do is go between two countries in Europe for vacation, maybe not so much, but business travel, travel to poor countries, it’s going to be radically disrupted regardless of what happens at this point with the pandemic. So that’s already sort of written in.


Family life. I think you can say some aspects have become more intense. Time together has increased. Some aspects have become less intense. So time together with extended family has been decreased. So I would expect certain things to follow from that, like people prioritizing family oriented leisure. If you couldn’t see your grandparents for the last two years, then your next vacation. Very well, maybe to visit your grandparents instead of going to Euro Disney.

And so that will change people’s priorities. Short term priorities. As far as making up for things they lost, people are getting together to have graduation parties for the graduations they missed. So there’s a big backlog of things weddings, baby showers, things that are celebrations or things that people consider to be milestones or life events that they don’t want to just lose. If you lost a breakfast at your favorite restaurant, you don’t have to make that up. But if you lost your grandmother’s 80th birthday party, that might be something that you do make up. So I think there’s a lot of catch up to be done that we’ll see in social life.

That relates actually to the inequality issue. To some degree, the first evidence we saw the sort of supply chain issues that are beguiling us so much now in the US were actually construction related things like lumber that started right away. And that’s when we realized that people were rich.

People who were staying home were renovating their homes a lot already in the pandemic, which seems sort of counterintuitive, like, normally, that’s something you do during economic good times and so on. But then we saw real estate prices going up. So we see that for people whose incomes were not disrupted, their consumption didn’t decline. And in fact, it may have shifted to be more active in some respects, especially in the home sphere. People investing in improving their homes and furnishings

Take me and my home office. I mean, I painted the wall green — by the time I painted the wall green that means I was thinking about a semipermanent situation in my house. So this used to be the guest room. Now it’s the office.

That’s very minor. But that’s indicative of the sort of the changes that people made that have their own momentum and some of them become permanent.

Fear and uncertainty

So people becoming more home oriented seems somewhat inevitable, but also the fear and uncertainty. It’s very unpredictable what that does. But I think it’s inevitable that we’ll have more. I don’t know if you remember, there was sort of a meme in 2020 that was sort of like, oh, how could this year get any worse? And then the joke was like, 2021 is the same. And I think part of that is just coping with the reality of a baseline shift in risk of catastrophic things.

So now climate change events that are unambiguously attributable to climate change are more frequent just over the last few years. Maybe it’s just our consciousness to some degree, but it certainly is the perception that, oh, this is going to change. Oh, this is the erosion of democracy. This is the fear of global health crisis.

People already talking about things like the next pandemic. I heard today that they’re changing the way the doors work on the buses in our city to allow people to enter from the rear, which was a problem during the pandemic because they wanted people to enter from the rear so that the bus driver wouldn’t have to face everybody and have risk. Well, they said on the news today, it also will be helpful in a future public health crisis to make this change. Well, we never cared that much about preparing for public health crisis before, so now we do.

Polarization and culture wars

I think what both the mask and the vaccine things show us — which are both such ridiculous issues to have culture wars over — I think what it shows us is that we’ll do it over anything. So even if we don’t continue to have politically polarized, culturally divisive conflicts over masks and vaccines, we’ll find the next topic.

That’s a reality that we have to anticipate beyond the pandemic. I think barring an extreme evolutionary development by the virus itself, we’re not going back to this sort of mass death event of the early pandemic. But again, if we realize how much our baseline has shifted, even if we’re making 5% to 10% adjustments, it’s still huge. And I think the people’s sense of what they would call ontological security, like the sense that I know how the world works, is disrupted.

And I think the polarization and cultural war stuff that we see is partly reaction to that. It always was partly reaction to social change. It always was, oh, some people saying, Why do you have to change the society so much? Why do we need the Internet? Why do we need affirmative action? Why do we need immigration? Why can’t we just have the world the way it used to be? So to the extent that social change is accelerated, then the culture war stuff inevitably will be, too.

And I can also add this is geopolitical, which is really not my area. But some of this is stoked by conflict between countries. So like the Russian intervention and the US political system — there are just opportunities for people to make mischief deliberately. Once we have exposed this vulnerability, once we expose that we’re prone to turn anything into a culture war, then it’s easy for anybody to take advantage of that, whether it’s companies with simple commercial objectives or countries with massive geopolitical ambitions.

One of the irrationalities about people react how people react to the virus is that they tend to be more afraid they’ll catch the virus from people who are not like them. So people don’t wear masks around their neighborhood, although they might when they leave the neighborhood. I think that perception is just sort of other people are more scary.

Diversity and social change

The race and ethnic equity and diversity issues become wrapped up in whatever else is going on. The fact that the Black Lives Matter protests were so enhanced during the Pandemic year was not an accident because it was a sense of things being a dramatic change and uncertainty, and people not liking the people have had enough.

So I think that continues. I do think there are generational changes in that which we haven’t yet grappled with. You can see this a little bit with how different young people’s attitudes are about gender and gender identity to older people, how fast something so fundamental shifted that a large portion of young people have a very different attitude toward gender identity than five years ago.

Generational change is very important. And if we think about how this has changed during the pandemic times, I think it had to do with how old you were when the pandemic came. And so how you were affected. Kids who were in school and had to switch school at home will be permanently affected. We don’t know exactly how much, but the impacts on academic achievement have been pretty dramatic in the US and very unequal. So low income and minority kids lost more reading and math and science development than richer kids. And that’s in an amount that would have shocked us before. And you can get some of that back. But you can’t get that all back.

I think if you look at the mental health data on young people, just a phenomenal crisis in terms of depression and anxiety, suicidal ideation. Young people’s mental health is in trouble. I see it in my students, and we see it in the data. And so that stays with them to some degree that experience stays with them forever. But in terms of cultural shifts, like the kind of things people think about with generational change, or are they more progressive? Are they more open minded?

Are they becoming more entitled, more spoiled and all those issues? I think a lot of that is really just age related, not generational. It’s old people thinking, kids these days. But there are some things that change.

The baby boom generation, especially in the US, was a generation that experienced change more than over the course of their lives more than other people. So if you were born in the you were born into that stereotypical 1950s family, those people are the people who destroyed that in their own lives. So just in the course of one generation, they were born in the 1950s family, and then they created the 80s family.

They’re getting older. So they get more conservative in some sense. But they’ve changed the way we do old age things that old people do now that the baby who brought us include divorce, include coming out as transgender, being more willing to adopt other kinds of family forms, like cohabitation, like living apart together, the whole attitude towards sex at older age. Those things came from that generation. And those things are young people can look forward to moving into that kind of old age. It’s harder to see what today’s young people are going to bring into older ages.

Demographic change

So what I said before about giant change, slipping back into just very large change, I think maybe what we see. So the overall birth rate decline in the US in 2020 was about 4%, which is the biggest one year change in 50 years. So that was crisis response.

But we’re still seeing lower birth rates. We already were seeing lower birth rates. So it’s a question of how the pandemic merged with existing trends. And here I’ll go back to the slow thing. Demographic things all slowed down except death, birth slowed down, marriage, divorce even slowed down and migration, at least immigration migration within countries.

Even if those things head back towards normal, the shifts that we saw were pretty big. If you look sort of between November and February, that four-month period birth rates were probably down in the US more like 10%. So half a year of a 10% decline is a very big ripple, no matter what.

And you can’t get that back. That’s the way birth rates work is even if you can’t have more babies born last year, no matter what you do. Even if birth rates come back. And I think probably what we’re going to see with birth rates is a combination of some births that we make a distinction between quantum and tempo, between births that are permanently lost and birds that are delayed. And there’s a relationship between them. If all young people together decide not to have a baby this year, and then they all decide that they will do it next year, some of them won’t.

So there’s a relationship between delay and total and total decline that we’ll definitely see. So birth rates are going to be down. And so that means population growth so slow. That means populations will continue to age. And even if it’s a short term effect, it’s contributing to the longer term trend in that direction in all developed societies, for sure.

When we look at the other demographic things like marriage and divorce, it’s the kind of thing where you could see a rebound that makes up for those things. I think when housing prices go down, it’s harder to divorce because you have to sell your house to divorce. On the other hand, high house prices give certain people opportunities. Okay, so if there’s, like, a roaring 20s reaction and we’re all thrilled and excited when this is all over, you could see a rebound of certain things like marriage. But so far, there’s no sign of that. We had a huge decline in 2020, and it’s come back a little bit, but it has nothing to make up. So we lost a lot of marriages, maybe forever.

And then when it comes to migration, in terms of the wealthy societies, immigration was the only hedge against population decline. And if the culture turns more against immigration, either because of racism and nativism or because the pandemic prohibits travel and stuff, then that means that our ability to respond to population decline is reduced.

So population decline seems pretty inevitable in the rich countries being accelerated by these events.

Policy and economics

Well, I do think there’s a possibility on health, a good possibility that this whole thing in the US pushes us more in the direction of paying attention to public health and maybe even access to health. I was really intrigued that everybody assumed that COVID related testing and vaccination, of course, would be free. There’s no reason that COVID stuff should be free, but cancer treatment is not. It’s just that it happened so fast and we had to deal with it. It’s sort of like we learned how important healthcare is.

So, of course, Americans, no offense, are terrible at learning lessons, but it’s possible some people there’s possibly positive direction, positive change in some of that area. We’ll understand the public responsibility for things like healthcare. I am afraid that for personal relationships and romantic relationships and families, it’s mostly damage. So even if there’s sort of silver linings and people come to appreciate the good things in life and so on, those are all rebound effects from trauma and so they don’t overcome the bad things. I don’t see that happening anyway.

So if you think about the vulnerability and fear and heartache and all those things, I don’t know, I guess I think people will overreact will overreact to things in positive and negatively. So if you’re trying to predict people’s behavior, it probably gets harder and riskier. Well for white collar and middle class people. Certainly a shift. The working at home is not going away.

And it’s very class skewed. It’s not only related to income and status, but it’s highly correlated and it’s not changing. I mean, it’s not going all the way back.

So that’s very big. When we talk about the great resignation and people quitting their jobs again. Remember the scale, if we have a 10% increase in unemployment for a few months, that’s extreme. And we shouldn’t expect that to ever happen again. So if a few million people quit their jobs in anger, that might be a one-time thing. But if quitting your job in anger becomes even 5% more likely in the coming years, that’s very noticeable from a business perspective. And so I think some of that continues inevitably. So I think it fits into the pattern of diversity where we will see some people happy, attached, risk averse stay in their jobs. And some people fed up disgruntled, unable to accept frustration, will quit their jobs. And if the baseline is nobody quit their job and you can’t quit your job less than zero amount.

So if the experience diverges, it shows up as a rising average, because even if some people love their jobs more, they can’t quit their jobs less than zero. So inevitably we have more people quitting their jobs, even if what’s driving that is just a greater unpredictability to work. So if you’re expecting your employees to stick around, you’re going to have more of them quitting anyway. Yes, definitely more people quitting with technology and Zoom and all that. And like this, more work being outsourced and including geographically. So that is going to include international call centers and all that stuff that was already happening. People reading your chest X ray in India and all that is only going to happen more and more.

One thing I did want to mention is that global travel being reduced changes people’s perspective on things, even if not everybody travels globally between countries, those that do have a disproportionate impact, even if it’s only middle class or rich people who travel to other countries for vacations.

Those people have more impact on the culture than poor people. And so the loss of that and the fact that the pandemic is diverging between rich and poor countries means that travel is not coming back the way it was, and that’s bad for our attitudes, our open mindedness, our cultural integration, like all those things, are undermined by the loss of global travel, which I think we’re going to have for quite a while.

Youth power

If you look at in the US when we had was that rash of school shootings and that generation very short generation, a few years of young people who are super into gun control and were great activists and brilliant spokespeople or like Greta Thornberg with the climate change.

These things maybe are ephemeral, like they come and go. But on the other hand, I would expect young people’s progressive, not everybody, but like a large portion of young people doing progressive things dramatically. I think that will only continue. And that’s great, mostly that’s for the good, even if it increases kind of generational conflict, generational conflict, probably in the long run, is a positive thing. Young people are usually more right than old people.

So climate change inevitably will be a huge part of that. But I don’t know what they’ll do next, whether it’s gender, race, climate change or whatever. But I think don’t expect that permanent presence of a surprising group of young people suddenly showing up and doing something dramatic. So I expect that to keep happening unpredictably. And I think that’s definitely good.


I think part of what happens as the Cold War fades is that the label doesn’t mean anything doesn’t have carries no negative connotation with young people anymore.

There’s no socialist country or society that is creating a negative example right now. Nobody really believes that China is Communist or whatever that doesn’t register with people who want more redistributive policies. So they don’t think, oh, no, we’ll become China if we raise taxes on Mark Zuckerberg. So to young people, that’s nonsensical to old people that still carries weight. But yes, and go back to the question of scale.

We spent a few trillion dollars on infrastructure. I think the idea of raising taxes 10% on rich people and redistributing that wealth will seem very, not shocking to young people. And so I do think that continues. And whether or not that actually becomes policy. I don’t know.

But I do think that the baseline has shifted on what’s an acceptable amount of economic disruption because doing things on a very large scale is not surprising. We just sent every kid home for over a year. So they’re not going to be shocked at the idea of a 10% tax increase on rich people, which would be totally revolutionized to welfare state in the US.

But in terms of stimulus and infrastructure, they’re pretty big. If they get the second one passed, then that could become baked in as new normal, a higher degree of infrastructure spending which us desperately needs. People do not realize. Americans have been very slow to realize how badly our infrastructure was failing. And I think Biden was very smart. And the Democrats were very smart to package all this other social stuff as infrastructure like elder care and prescription drugs and all that stuff.

Even if that doesn’t radically change people’s ideology in some ways, even if they just successfully spend that money, it will have a large effect. So that does mean things like Internet and airports and things like that could be improved, which are positive, even if they don’t, even if they’re not exciting on social media. I do think those things are pretty big. It’s not gone. It turns out the people who said Trump was just a symptom of a larger problem were right.


And so even if Trump died today, I don’t think it’s not going away. And what it means in politics is virulent racist nationalism is probably increasing. It means respect for democratic norms is less stable or secure than in the past.

And that also increases. And it means in terms of my kind of work, like social science and science in general, it means the science denialism, the undermining of the scholarship fascism like to tell us the authoritarians want to undermine truth itself. They don’t want us to be able to have a discourse that has any rational basis. And I think that continues when you look at the politicians. One thing the Democrats still haven’t learned is that explaining to the public that the Republicans are hypocrites doesn’t hurt them. They don’t care, the public doesn’t care, and the politicians don’t care. So that just increases. And in Europe, the far right nationalism has the added feature of being related to conflict between countries, especially Russia. And so it just continues to be stoked. So I think that’s bad.

And it continues. And in terms of democratic values such as they are, I think it’s quite bad.


I think a lot of the way technology gets into our heads is usually unconscious. And so one of the reasons why people are so angry at Silicon Valley and social media companies and things is because they always seem to know where we’re going before we get there. And it’s partly because they build us the ramp to get to the next place we’re going. So when Facebook introduced the Like button, nobody realized that that was going to change the way the Internet works for everybody.

So things like that keep happening. I don’t put much stock in the Mark Zuckerberg Metaverse at the moment, but on the other hand, I do think the people who will determine that are not us.

So the way we cope with these changes is by using technological tools. On the other hand, we’re stuck using the tools they give us. And I think that’s sort of true if you look at the smart technologies, the Internet of things, the things that connect everything to each other. I think people don’t realize how much of that capacity is becoming already part of our regular lives.

So even if it’s just your watch knows what your phone knows what your computer knows, what your thermostat knows what your car is doing, those things. It’s unpredictable, like we don’t realize we need those things, but we’re going to get used to them more and more. And the way that they make people want those things is by sort of the quantification of self. So like your watch tells you your calories and your steps that you’re breathing and your heart rate and also your consumer confidence and your insurability that stimulates people’s competitive thinking and their sense of responsibility sort of what they would call neoliberalism: if you fail, it’s your fault. The more people believe that, the more they want stuff like a daily score.

I think if the people trying to sell this technology are going to have figured this out, that you do it in the sense of giving people the illusion of control and self improvement and all that, that’s what people think they want. So you want your car to tell you that you haven’t taken enough steps today and they don’t realize that that involved that technologically.

What that means is that everything has to communicate with everything else. So they’ll tell us what we need and then we’ll demand it.

The bottom line

I still think it’s inequality. I mean, we were already upset about inequality, the people who were concerned. But even if you’re not upset about inequality, what it does is it widens the gap in perception and experience.

We’ve said before, if inequality increases crime and crime increases fear, then inequality is bad for rich people, too. It makes them afraid and anxious. And that’s maybe metaphorical. But I think it’s really true. So the divergence in perception is just large.

And I think you see it in sort of what Andy Slav at the public health expert, called in his book, the room service lockdown. Some people were locked down and some people were delivering them things. And I think Bob Dylan said in the whole world, like some of us are prisoners and some of us are guards.

It’s polarization in the literal sense of just extreme differences in experience. And so that undermines all kinds of social things. But I also think it just becomes a source of stress. And I think it contributes to the mental health problems. Honestly, if you interact with people that have a very different perception of life than you, it’s just harder to relate to them. And people are social, and they need to relate to each other. And so the widening gulf in experience between different groups just makes social life more tense and more difficult. And so I’m sorry to have my main social trend to be so negative. But I do think it is mostly negative. And that’s then to the extent that good things happen, it’s in response to that.

I’m optimistic about young people, that’s always the potential. But I do think that the underlying thing that we’re reacting to is the shift. Inequality, not just economic but in difference in experience and perception.

Demographic facts all students should know right now

Here’s the 2021 update of a series I started in 2013. A few pandemic-specific facts below.

If anyone tells you that “facts are useless in an emergency,” give them a bad grade. Knowing basic demographic facts lets us run a quick temperature check on the pot we’re slowly boiling in — which we need to survive. The idea is to get your radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it (and tell the same stories over and over).

This year, in pursuit of this mission, I created the Demographic Fact A Day Twitter account, which started tweeting one fact per day at the start of 2021. Some of these are more advanced, some very simple. Here’s a figure from that account, for a taste:


Everyone likes a number that appears to support their perspective. But that’s no way to run (or change) a society. The trick is to know the facts before you create or evaluate an argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.

The list below are demographic facts you need just to get through the day without being grossly misled or misinformed — or, in the case of journalists or teachers or social scientists, not to allow your audience to be grossly misled or misinformed. Not trivia that makes a point or statistics that are shocking, but the non-sensational information you need to make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 266 million and 400 million!).

This is only a few dozen facts, not exhaustive but they belong on any top-100 list. This year, many of the most important facts are about the pandemic, but they’re not included here — these are some of what you need to understand the upheavals of the day. Feel free to add additional facts in the comments (as per policy, first-time commenters are moderated).

The numbers are rounded to reasonable units for easy memorization. All refer to the US unless otherwise noted. Most of the links will take you to the latest data:

World Population7.8 billion1
U.S. Population333 million1
Children under 18 as share of pop.22%2
Adults 65+ as share of pop.17%2
Official unemployment rate (July 2021)5%3
Unemployment rate range, 1970-20183.9% – 15%3
Labor force participation rate, age 16+62%9
Labor force participation rate range, 1970-201760% – 67%9
Non-Hispanic Whites as share of pop.60%2
Blacks as share of pop.13%2
Hispanics as share of pop.19%2
Asians / Pacific Islanders as share of pop.6%2
American Indians as share of pop.1%2
Immigrants as share of pop14%2
Adults age 25+ with BA or higher32%2
Median household income$62,8002
Total poverty rate11%8*
Child poverty rate14%8*
Poverty rate age 65+9%8*
Most populous country, China1.4 billion5
2nd most populous country, India1.3 billion5
3rd most populous country, USA (CIA estimate)335 million5
4th most populous country, Indonesia275 million5
5th most populous country, Pakistan238 million5
U.S. male life expectancy at birth756
U.S. female life expectancy at birth806
Life expectancy range across countries53 – 877
World total fertility rate2.410
U.S. total fertility rate1.710
Total fertility rate range across countries0.9 – 6.810

* These are pre-pandemic poverty rates.


1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

10. World Bank

Alexa devaluation, cutting room floor edition

Joe Pinsker at the Atlantic has written, “Amazon Ruined the Name Alexa,” that develops the story of the name, which I started tracking with a pick drop in 2017, writing: “You have to feel for people who named their daughters Alexa, and the Alexas themselves, before Amazon sullied their names. Did they not think of the consequences for these people? Another bad year for Alexa. After a 21.3% drop in 2016, another 19.5% last year.”

Pinsker concludes:

Amazon did not exactly ruin the life of every Alexa, but the consequences of its decision seven years ago are far-reaching—roughly 127,000 American baby girls were named Alexa in the past 50 years, and more than 75,000 of them are younger than 18. Amazon didn’t take their perfectly good name out of malice, but regardless, it’s not giving it back.

From the peak year of 2015, when there were 6,050 Alexas born in the US, the number fell 79% to 1272 in 2020, the biggest drop among names with at least 1000 girls born in 2015. Here’s that list:

Pinsker got Amazon on the record not commenting on the problem they created for actual humans named Alexa, who he reports are being bullied in school — they are not only named after a robot, but a subservient female one, so no surprise. Amazon said only, “Bullying of any kind is unacceptable, and we condemn it in the strongest possible terms.”

Cutting room floor

I am only quoted in the story saying, “We don’t usually think about the individuals who are already born when this happens, but the impact on their lives is real as well.” No complaint about that, of course. But since my interview with Pinsker was over email, I can share my other nuggets of insight here, with his questions:

I saw that you first blogged about this in 2018 (when you were remarking on the 2017 name data). Did you just happen to stumble upon Alexa’s declining popularity yourself, or did someone else point it out to you?

I wrote a program that identifies that names with biggest changes, and Alexa jumped out. One interesting thing about naming patterns is that dramatic changes are quite rare. Names rise and fall over time, but they rarely show giant leaps or collapse as dramatically as Alexa did after 2015.

When you look at what has happened to the name Alexa since Amazon’s Alexa was released in late 2014, how much of the name’s declining popularity do you attribute to Amazon? (Is it common for names to plummet in popularity as quickly as Alexa has since 2014?)

The Social Security national name data is a mile wide and an inch deep. We have a tremendous amount of name data, but it is all just counts of babies born — we have no direct information about who is using what names, or why. So any attribution of causal processes is speculative unless we do other research. That said, because dramatic changes are so rare, it’s usually pretty easy to explain them. For example, some classic 1970s hits apparently sparked name trends: Brandy (Looking Glass, 1972), Maggie (Rod Stewart, 1971), and of course Rhiannon (Fleetwood Mac, 1975). I defy you to find someone named Rhiannon, born in the US, who was born before 1975. We can also observe dramatic changes even among uncommon names, such as a doubling of girls named Malia in 2009 (the Obamas’ daughter’s name).

At one point, you mentioned on your blog that Hillary was another name that became less popular after becoming culturally ubiquitous. Are there any other examples you’re aware of, where a name’s cultural ubiquity tanks its popularity?

On the other hand, there are disaster stories, like Alexa. Hillary was rising in popularity before 1992, and then tanked. Monica declined dramatically after 1998 (after the Clinton sex scandal). Ellen became much less common suddenly the year after Ellen DeGeneres came out as gay in 1997. And Forrest, which had been on the rise before 1994, plummeted after Forrest Gump came out and virtually disappeared.

We don’t usually think about the individuals who are already born when this happens, but the impacts on their lives is real as well. The name trends tell us something about the social value of a name (and unlike other commodities, in the US at least there is no limit to the number of people who can have a name). People who were named Adolph before Hitler, Forrest before Forrest Gump, or Alexa before Amazon live with the experience of a devalued name. Many of them end up changing their names or using nicknames — or just getting used to people making jokes about their name every time they meet someone new, have attendance called, or go to the department of motor vehicles.

If I’m reading the SSA data correctly, there were 1,272 Alexas born last year in the U.S. I know this is speculative, but would you guess that most of these parents aren’t aware of the name of Amazon’s device? Or is it that they’re aware, and just don’t care?

Some don’t know, some don’t care, some probably think it’s cool. For some it may be a family name. I am fascinated to see that Alexis and Alexia have also seen five-year declines of more than 60% in name frequency. I wonder if that is because of concern over Alexa devices mishearing those names — certainly a reasonable concern — or maybe just association with the product making those names seem derivative or tacky. It’s hard to say.

See all the posts about names under the tag.

Pew response and attempted clarification

First, a response from Pew, then a partial data clarification on generations. In response to my Washington Post Op-Ed on generations, Generation labels mean nothing. It’s time to retire them, Kim Parker, the director of social trends research at the Pew Research Center, published a letter that read:

Philip N. Cohen criticized the use of generation labels. Generations are one of many analytical lenses researchers use to understand societal change and differences across groups. While there are limitations to generational analysis, it can be a useful tool for understanding demographic trends and shifting public attitudes. For example, a generational look at public opinion on a wide range of social and political issues shows that cohort differences have widened over time on some issues, which could have important implications for the future of American politics.

In addition, looking at how a new generation of young adults experiences key milestones such as educational attainment, marriage or homeownership, compared with previous generations in their youth, can lend important insights into changes in American society.

To be sure, these labels can be misused and lead to stereotyping, and it’s important to stress and highlight diversity within generations. At Pew Research Center, we consistently endeavor to refine and improve our research methods. Therefore, we are having ongoing conversations around the best way to approach generational research. We look forward to engaging with Mr. Cohen and other scholars as we continue to explore this complex and important issue.

Kim Parker, Washington

I was happy to see this, and look forward to what they come up with. I am also glad to see that there has been no substantial defense of the current “generations” research regime. Some people on social media said they kind of like the categories, but no researcher has said they make sense, or pointed to any research justifying the current categories. With regard to her point that generations research is useful, that was in our open letter, and in my op-ed. Cohorts (and, if you want to call a bunch of a cohorts a generation, generations) matter a lot, and should be studied. They just shouldn’t be used with imposed fixed categories regardless of the data involved, and given names with stereotyped qualities that are presumed to extend across spheres of social life.

Several people have asked me for suggestions. My basic suggestion is to do like you learned in social science class, and use categories that make sense for a good reason. If you have no reason to use a set of categories, don’t use them. Instead, use an empty measure of time, like years or decades, as a first pass, and look at the data. As I argued here, there is not likely to be a set of birth years that cohere across time and social space into meaningful generational identities.

Data question

In the Op-Ed, I wrote this: “Generation labels, although widely adopted by the public, have no basis in social reality. In fact, in one of Pew’s own surveys, most people did not identify the correct generation for themselves — even when they were shown a list of options.” The link was to this 2015 report titled, “Most Millennials Resist the ‘Millennial’ Label” (which of course confirms a stereotype about this supposed generation). I was looking in particular at this graphic, which I have shown often:

It doesn’t exactly show what portion of people “correctly” identify their category, but I eyeballed it and decided that if only 18% of Silents and 40% of Millennials were right, there was no way Gen X and Boomers were bringing the average over 50%. Also, people could choose multiple labels, so those “correct” numbers was presumably inflated to some degree by double-clickers. Anyway, the figure doesn’t exactly answer the question.

The data for that figure come from Pew’s American Trends Panel Wave 10, from 2015. The cool thing is you can download the data here. So I figured I could do a little analysis of who “correctly” identifies their category. Unfortunately, the microdata file they share doesn’t include exact age, just age in four categories that don’t line up with the generations — so you can’t replicate their analysis.

However, they do provide a little more detail in the topline report, here, including reporting the percentage of people in each “generation” who identified with each category. Using those numbers, I figure that 57% selected the correct category, 26% selected an incorrect category, 9% selected “other” (unspecified in the report), and 8% are unaccounted for. So, keeping in mind that people can be in more than one of these groups, I can’t say how many were completely “correct,” but I can say that (according to the report, not the data, which I can’t analyze for this) 57% at least selected the category that matched their birth year, possibly in combination with other categories.

The survey also asked people “how well would you say they term [generation you chose] applies to you?” If you combine “very well” and “fairly well,” you learn, for example, that actual “Silents” are more likely to say “Greatest Generation” applies well to them (32%) than say “Silent” does (14%). Anyway, if I did this right, based on the total sample, 46% of people both “correctly” identified their generation title, and said the term describes them “well.” I honestly don’t know what to make of this, but thought I’d share it, since it could be read as me misstating the case in the Op-Ed.

What the editors of 6000 journals tell us about gender, international diversity, open access, and research transparency

Micah Altman and I have written a paper using the new Open Editors dataset from Andreas Pacher, Tamara Heck, and Kerstin Schoch. They scraped up data on almost half a million editors (editors in chief, editors, editorial board member) at more than 6000 journals from 17 publishers (most of the big ones; they’ve since added some more). Micah and I genderized them (fuzzily), geolocated them in countries, and then coded the journals as either open access or not (using the Directory of Open Access Journals), and according to whether they practice transparency in research (using the Transparency and Openness Promotion signatories). After just basic curiosity about diversity, we wondered whether those that practice open access and research transparency have better gender and international diversity.

The results show overwhelming US and European dominance, not surprisingly. And male dominance, which is more extreme among editors in chief, across all disciplines. Open access journals are a little less gender diverse, and transparency-practicing journals a little more internationally diverse, but those relationships aren’t strong. There are other differences by discipline. A network analysis shows not much overlap between journals, outside of a few giant clusters (which might indicate questionable practices) although it’s hard to say for sure — journals should really use ORCIDs for their editors. Kudos to Micah for doing the heavy lifting on the coding, which involved multiple levels of cleaning and recoding (and for making the R markdown file for the whole thing available).

Lots of details in the draft, here. Feedback welcome!

Here are the editors, by country:

During the pandemic year of 2020, thousands of US parents named their babies Kobe and Gianna

And a few other highlights.

Data from the Social Security Administration show that the names Kobe and Gianna had the greatest increase in popularity of any names in the country in 2020; as Kobe boys increased from 499 to 1500 and Gianna girls from 3408 to 7826. Kobe Bryant and his daughter Gianna died in a helicopter crash on January 26 last year, one of the dramatic national news events eclipsed by the pandemic (George Floyd’s daughter, now 7 years old, is also named Gianna).

The Kobe count of 1500 was surpassed only in 2001, during his first run of NBA championships, but the number per 1000 births was higher in 2020. Here is the trend:

And the Gianna trend, with a similar increase off a much higher base. Gianna became the 12th most common name given to girls in 2020.

Other news from the pandemic year in naming

Besides Gianna, not much change in the top 20 names, by gender, as Olivia, Emma, Liam, and Noah continued their dominance. Most of the top 20 names declined in popularity last year.

Outside the top names, the biggest drop in percentage terms (among those with at least 1000 births) was Alexa, who fell another 36%, from 1995 to 1272. Alexa has had a historically catastrophic decline since Amazon gave the name to its robot shopping companion (discussed last year).

Finally, Mary remains dormant, with 2188 girls getting the name in 2020, a drop of 21 from 2209. I told the story of Mary going back to the Revolutionary War on this blog and in Enduring Bonds. Still ripe for a comeback (jinx). Here’s an updated Figure 1:

The Social Security Data and Stata code for this analysis is here under CC0 license: Note SSA updates their denominators every year; I have a file of those in here too.

Why you’ll never establish the existence of distinct “generations” in American society

An update from Pew, today’s thoughts, and then another data exercise.

Pew response

After sending it the folks in charge at the Pew Research Center, I received a very friendly email response to our open letter on generation labels. They thanked me and reported that they already had plans to begin an internal discussion about “generational research” and will be consulting with experts as they do, although the timeline was not given. I take this to mean we have a bona fide opportunity to change course on this issue, both with Pew (which has outsized influence) and more widely in the coming months. But the outcome is not assured. If you agree that the “generations” labels and surrounding discourse are causing more harm than good, for researchers and the public, I hope you will join with me and 140+ social scientists who have signed the letter so far, by signing and sharing the letter (especially to people who aren’t on Twitter). Thanks!

avocado toast

Why “generations” won’t work

Never say never, but I don’t see how it will be possible to identify coherent, identifiable, stable, collectively recognized and popularly understood “generation” categories, based on year of birth, that reliably map onto a diverse set of measurable social indicators. If I’m right about that, which is an empirical question, then whether Pew’s “generations” are correctly defined will never be resolved, because the goal is unattainable. Some other set of birth-year cutoffs might work better for one question or another, but we’re not going to find a set of fixed divisions that works across arenas — such as social attitudes, family behavior, and economic status. So we should instead work on weaning the clicking public from its dependence on the concept and get down to the business of researching social trends (including cohort patterns), and communicating about that research in ways that are intelligible and useful.

Here are some reasons why we don’t find a good set of “generation” boundaries.

1. Mass media and social media mean there are no unique collective experiences

When something “happens” to a particular cohort, lots of other people are affected, too. Adjacent people react, discuss, buy stuff, and define themselves in ways that are affected by these historical events. Gradations emerge. The lines between who is and is not affected can’t be sharply drawn by age.

2. Experiences may be unique, but they don’t map neatly onto attitudes or adjacent behaviors

Even if you can identify something that happened to a specific age group at a specific point in time, the effects of such an experience will be diffuse. To name a few prominent examples: some people grew up in the era of mass incarceration and faced higher risks of being imprisoned, some people entered the job market in 2009 and suffered long-term consequences for their career trajectories, and some people came of age with the Pill. But these experiences don’t mark those people for distinct attitudes or behaviors. Having been incarcerated, unemployed, or in control of your pregnancy may influence attitudes and behaviors, but it won’t set people categorically apart. People whose friends or parents were incarcerated are affected, too; grandparents with unemployed people sleeping on their couches are affected by recessions; people who work in daycare centers are affected by birth trends. And, of course, African Americans have a unique experience with mass incarceration, rich people can ride out recessions, and the Pill is for women. When it comes to indicators of the kind we can measure, effects of these experiences will usually be marginal, not discrete, and not universal. (Plus, as cool new research shows, most people don’t change their minds much after they reach adulthood, so any effects of life experience on attitudes are swimming upstream to be observable at scale.)

3. It’s global now, too

Local experiences don’t translate directly to local attitudes and behavior because we share culture instantly around the world. So, 9/11 happened in the US but everyone knew about it (and there was also March 11 in Spain, and 7/7 in London). There are unique things about them that some people experienced — like having schools closed if you were a kid living in New York — but also general things that affected large swaths of the world, like heightened airline security. The idea of a uniquely affected age group is implausible.

4. Reflexivity

Once word gets out (through research or other means) about a particular trait or practice associated with a “generation,” like avocado toast or student debt, it gets processed and reprocessed reflexively by people who don’t, or do, want to embody a stereotype or trend for their supposed group. This includes identifying with the group itself — some people avoid it and some people embrace it, and some people react to who does the other things in other ways — until the category falls irretrievably into a vortex of cultural pastiche. The discussion of the categories, in other words, probably undermines the categories as much as it reinforces them.

If all this is true, then insisting on using stable, labeled, “generations” just boxes people into useless fixed categories. As the open letter puts it:

Predetermined cohort categories also impede scientific discovery by artificially imposing categories used in research rather than encouraging researchers to make well justified decisions for data analysis and description. We don’t want to discourage cohort and life course thinking, we want to improve it.

Mapping social change

OK, here’s today’s data exercise. There is some technical statistical content here not described in the most friendly way, I’m sorry to say. The Stata code for what follows is here, and the GSS 1972-2018 Cross-Sectional Cumulative Data file is free, here (Stata version); help yourself.

This is just me pushing at my assumptions and supplementing my reading with some tactile data machinations to help it sink in. Following on the previous exercise, here I’ll try out an empirical method for identifying meaningful birth year groupings using attitude questions from the General Social Survey, and then see if they tell us anything, relative to “empty” categories (single years or decades) and the Pew “generations” scheme (Silent, Baby Boom, Generation X, Millennials, Generation Z).

I start with five things that are different about the cohorts of nowadays versus those of the olden days in the United States. These are things that often figure in conversations about generational change. For each of these items I use one or more questions to create a single variable with a mean of 0 and a standard deviation of 1; in each case a higher score is the more liberal or newfangled view. As we’ll see, all of these moved from lower to higher scores as you look at more recent cohorts.

  • Liberal spending: Believing “we’re spending too little money on…” seven things: welfare, the environment, health, big cities, drug addiction, education, and improving the conditions of black people. (For this scale, the measure of reliability [alpha] is .66, which is pretty good.)
  • Gender attitudes: Four questions on whether women are “suited for politics,” working mothers are bad for children, and breadwinner-homemaker roles are good. High scores mean more feminist (alpha = .70).
  • Confidence in institutions: Seven questions on organized religion, the Supreme Court, the military, major companies, Congress, the scientific community, and medicine. High scores mean less confidence (alpha = .68).
  • General political views from extremely conservative to extremely liberal (one question)
  • Never-none: People who never attend religious services and have no religious affiliation (together now up to about 16% of people).

These variables span the survey years 1977 to 2018, with respondents born from 1910 to 1999 (I dropped a few born in 2000, who were just 18 years old in 2018, and those born before 1910). Because not all questions were asked of all the respondents in every year I lost a lot of people, and I had to make some hard choices about what to include. The sample that answered all these questions is about 5,500 people (down from almost 62,000 altogether — ouch!). Still, what I do next seems to work anyway.

Clustering generations

Once I have these five items, I combine them into a megascale (alpha = .45) which I use to represent social change. You can see in the figure that successive cohorts of respondents are moving up this scale, on average. Note that these cohorts are interviewed at different points in time; for example, a 40-year-old in 1992 is in the same cohort as a 50-year-old in 2002, while the 1977 interviews cover people born all the way back to 1910. That’s how I get so many cohorts out of interviews from just 1977 to 2018 (and why the confidence intervals get bigger for recent cohorts).

The question from this figure is whether the cohort attitude trend would be well served by some strategic cutpoints to denote cohorts (“generations” not in the reproductive sense but in the sense of people born around the same time). Treating each birth year as separate is unwieldy, and the samples are small. We could just use decades of birth, or Pew’s arbitrary “generations.” Or make up new ones, which is what I’m testing out.

So I hit on a simple way to identify cutpoints using an exploratory technique known as k means clustering. This is a simple (with computers) way to identify the most logical groups of people in a dataset. In this case I used two variables: the megascale and birth year. Stata’s k means clustering algorithm then tries to find a set of groups of cases such that the differences within them (how far each case is from the means of the two variables within the group) are as small as possible. (You tell it k, the number of groups you want.) Because cohort is a continuous variable, and megascale rises over time, the algorithm happily puts people in clusters that don’t have overlapping birth years, so I get nicely ordered cohorts. I guess for a U-shaped time pattern it would put young and old people in the same groups, which would mess this up, but that’s not the case with this pattern.

I tested 5, 6, and 7 groups, thinking more or fewer than that would not be worth it. It turns out 6 groups had the best explanatory power, so I used those. Then I did five linear regressions with the megascale as the dependent variable, a handful of control variables (age, sex, race, region, and education), and different cohort indicators. My basic check of fit is the adjusted R2, or the amount of variance explained adjusted for the number of variables. Here’s how the models did, in order from worst to best:

Cohort variable(s)Adjusted R2
Pew generations.1393
One linear cohort variable.1400
My cluster categories.1423
Decades of birth.1424
Each year individually.1486

Each year is good for explaining variance, but too cumbersome, and the Pew “generations” were the worst (not surprising, since they weren’t concocted to answer this question — or any other question). My cluster categories were better than just entering birth cohort as a single continuous variable, and almost as good as plain decades of birth. My scheme is only six categories, which is more convenient than nine decades, so I prefer it in this case. Note I am not naming them, just reporting the birth-year clusters: 1910-1924, 1925-1937, 1938-1949, 1950-1960, 1961-1974, and 1975-1999. These are temporary and exploratory — if you used different variables you’d get different cohorts.

Here’s what they look like with my social change indicators:

Shown this way, you can see the different pace and timing of change for the different indicators — for example, gender attitudes changed most dramatically for cohorts born before 1950, the falling confidence in institutions was over by the end of the 1950s cohort, and the most recent cohort shows the greatest spike in religious never-nones. Social change is fascinating, complex, and uneven!

You can also see that the cuts I’m using here look nothing like Pew’s, which, for example, pool the Baby Boomers from birth years 1946-1964, and Millennials from 1980 to 1996. And they don’t fit some stereotypes you hear. For example, the group with the least confidence in major institutions is those born in the 1950s (a slice of Baby Boomers), not Millennials. Try to square these results with the ridiculousness that Chuck Todd recently offered up:

So the promise of American progress is something Millennials have heard a lot about, but they haven’t always experienced it personally. … And in turn they have lost confidence in institutions. There have been plenty of scandals that have cost trust in religious institutions, the military law enforcement, political parties, the banking system, all of it, trust eroded.

You could delve into the causes of trust erosion (I wrote a paper on confidence in science alone), but attributing a global decline in trust to a group called “Millennials,” one whose boundaries were declared arbitrarily, without empirical foundation, for a completely unrelated purpose, is uninformative at best. Worse, it promotes uncritical, determinist thinking, and — if it gets popular enough — encourages researchers to use the same meaningless categories to try to get in line with the pop culture pronouncements. You get lots of people using unscrutinized categories, compounding their errors. Social scientists have to do better, by showing how cohorts and life course events really are an important way to view and comprehend social change, rather than a shallow exercise in stereotyping.


The categories I came up with here, for which there is some (albeit slim) empirical justification, may or may not be useful. But it’s also clear from looking at the figures here, and the regression results, that there is no singularly apparent way to break down birth cohorts to understand these trends. In fact, a simple linear variable for year of birth does pretty well. These are sweeping social changes moving through a vast, interconnected population over a long time. Each birth cohort is riven with major disparities, along the stratifying lines of race/ethnicity, gender, and social class, as well as many others. There may be times when breaking people down into birth cohorts helps understand and explain these patterns, but I’m pretty sure we’re never going to find a single scheme that works best for different situations and trends. The best practice is probably to look at the trend in as much detail as possible, to check for obvious discontinuities, and then, if no breaks are apparent, use an “empty” category set, such as decades of birth, at least to start.

It will take a collective act of will be researchers. teachers, journalists, and others, to break our social change trend industry of its “generations” habit. If you’re a social scientist, I hope you’ll help by signing the letter. (I’m also happy to support other efforts besides this experts letter.)

Note on causes

Although I am talking about cohorts, and using regression models where cohort indicators are independent variables, I’m not assessing cohort effects in the sense of causality, but rather common experiences that might appear as patterns in the data. We often experience events through a cohort lens even if they are caused by our aging, or historical factors that affect everyone. How to distinguish such age, period, or cohort effects in social change is an ongoing subject of tricky research (see this from Morgan and Lee for a recent take using the GSS) , but it’s not required to address the Pew “generations” question: are there meaningful cohorts that experience events in a discernibly collective way, making them useful groups for social analysis.

Open letter to the Pew Research Center on generation labels

I posted a draft of this, with a discursive preamble, yesterday. To see all the posts on generations, here’s the tag.

Sign the letter here.

We are demographers and other social scientists, writing to urge the Pew Research Center to stop using its generation labels (currently: Silent, Baby Boom, X, Millennial, Z). We appreciate Pew’s surveys and other research, and urge them to bring this work into better alignment with scientific principles of social research.

  1. Pew’s “generations” cause confusion.

The groups Pew calls Silent, Baby Boom, X, Millennial, and Z are birth cohorts determined by year of birth, which are not related to reproductive generations. There is further confusion because their arbitrary lengths (18, 19, 16, 16, and 16 years, respectively) have grown shorter as the age difference between parents and their children has lengthened.

  1. The division between “generations” is arbitrary and has no scientific basis.

With the exception of the Baby Boom, which was a discrete demographic event, the other “generations” have been declared and named on an ad hoc basis without empirical or theoretical justification. Pew’s own research conclusively shows that the majority of Americans cannot identify the “generations” to which Pew claims they belong. Cohorts should be delineated by “empty” periods (such as individual years, equal numbers of years, or decades) unless research on a particular topic suggests more meaningful breakdowns.

  1. Naming “generations” and fixing their birth dates promotes pseudoscience, undermines public understanding, and impedes social science research.

The “generation” names encourage assigning them a distinct character, and then imposing qualities on diverse populations without basis, resulting in the current widespread problem of crude stereotyping. This fuels a stream of circular debates about whether the various “generations” fit their associated stereotypes, which does not advance public understanding.

  1. The popular “generations” and their labels undermine important cohort and life course research

Cohort analysis and the life course perspective are important tools for studying and communicating social science. But the vast majority of popular survey research and reporting on the “generations” uses cross-sectional data, and is not cohort research at all. Predetermined cohort categories also impede scientific discovery by artificially imposing categories used in research rather than encouraging researchers to make well justified decisions for data analysis and description. We don’t want to discourage cohort and life course thinking, we want to improve it.

  1. The “generations” are widely misunderstood to be “official” categories and identities

Pew’s reputation as a trustworthy social research institution has helped fuel the false belief that the “generations” definitions and labels are social facts and official statistics. Many other individuals and organizations use Pew’s definitions in order to fit within the paradigm, compounding the problem and digging us deeper into this hole with each passing day.

  1. The “generations” scheme has become a parody and should end.

With the identification of “Generation Z,” Pew has apparently reached the end of the alphabet. Will this continue forever, with arbitrarily defined, stereotypically labeled, “generation” names sequentially added to the list? Demographic and social analysis is too important to be subjected to such a fate. No one likes to be wrong, and admitting it is difficult. We sympathize. But the sooner Pew stops digging this hole, the easier it will be to escape. A public course correction from Pew would send an important signal and help steer research and popular discourse around demographic and social issues toward greater understanding. It would also greatly enhance Pew’s reputation in the research community. We urge Pew to end this as gracefully as possible — now.

As consumers of Pew Research Center research, and experts who work in related fields ourselves, we urge the Pew Research Center to do the right thing and help put an end to the use of arbitrary and misleading “generation” labels and names.

In the war between armed police and the unarmed public, the police should unilaterally disarm.

Modified photo from Chris Yarzab:

One thing Duante Wright, Philando Castile, Walter Scott, Samuel DeBose, and Rayshard Brooks, have in common is that the police who killed them could have accomplished whatever they were legitimately supposed to be doing without a gun on their hip. The police in these incidents had no reason to anticipate violence in the interactions. There was no report of a violent crime, no weapons visible, no sign of anyone in imminent danger. Whether you think the police acted with racist malice, incompetence, or even reasonably, the fact is that if the police who killed them weren’t carrying guns no one would have died.

The structural approaches to police violence introduced in the last year, including reducing police funding to replace them with other agencies and services, involve big, complex proposals. For example, a recent law review article by Jordan Blair Woods reasonably suggests replacing police with unarmed civilian enforcers of traffic codes. These would require changing laws and restructuring government budgets.

A much simpler and immediately effective remedy to at least some of our problem is a simple matter of police department policy: don’t wear your guns.

Whether it was poor training, racism, malice, or just fatally bad luck that led Kimberly Potter to shoot Duante Wright with her gun instead of her Taser in Booklyn Center, Minnesota earlier this month, the body camera recording clearly shows she had nothing in her hands just seconds earlier. She didn’t enter the scene with her gun out because there was no reason to suspect violence, and in fact the only violence that occurred was her shooting Wright. If she hadn’t had a gun on her hip, he wouldn’t have died.

For all the talk of “de-escalation” in police interactions with the public, this simple solution is routinely overlooked. In any potentially violent conflict, the stakes are automatically raised to the level of the deadliest weapon present. Guns escalate conflict.

The policy details are important. In a society awash in guns (unlike many of those where police are usually unarmed), police here will sometimes need them for good reasons. You could start with some units dedicated to traffic enforcement, for example. Some police could have guns in a safe in the trunk of their car. Special units could be routinely armed. But the officers who come to your (my) house to discuss online death threats don’t need to be wearing firearms.

There are risks to police from such an approach, but the present default unreasonably assumes that carrying guns only reduces those risks. How often are unarmed police killed at traffic stops? If we don’t know the answer to that, maybe it hasn’t been sufficiently tried. If your response is, “one traffic cop killed is too many,” try applying that logic to the unarmed victims of police.

Even if you believe Darren Wilson, who said Michael Brown tried to take his gun in Ferguson, Missouri in 2014, possession of the gun was the basis of their violent conflict. Even if Darren Wilson had been just as racist in harassing Brown for walking in the street, no one would have died if Wilson hadn’t had a gun.

A Justice Department report on Michael Brown’s death noted, “Under well-established Fourth Amendment precedent, it is not objectively unreasonable for a law enforcement officer to use deadly force in response to being physically assaulted by a subject who attempts to take his firearm.” Well-established, perhaps, but that’s tragically circular – cop has a right to kill someone with his gun who tries to take his gun – because he has a gun.

If Duante Wright or Michael Brown or George Floyd had resisted arrest, punched an officer, or driven off to escape law enforcement, no one would have died. But that’s not all that would be different. If police in those situations, and millions of others, weren’t carrying guns, we could develop a new mutual understanding between the police and public: Police won’t “accidentally” kill you during a traffic stop or when reacting to nonviolent infractions, but if you do attack unarmed police, more police will show up later and they will have reason to be armed.

What might seem riskier to police upfront – leaving the gun in the trunk, or at the station – would certainly lead to fewer deaths of innocent, unarmed, nonviolent, people. Given the scale of innocent life taken in such incidents, and its effects on relations between the public and the police, that is a paramount concern for equity, civil rights, and law enforcement. But by reducing the stakes of individual interactions with police – automatically de-escalating them – it would probably also end up making the job safer for police as well.

Policing is dangerous work, work the police make more dangerous by introducing firearms into many interactions that should remain nonviolent. Would removing the holster from the standard uniform discourage people from becoming police? To some extent it might. But if not wearing a gun discouraged the kind of person for whom wearing a gun is the best part of the job, so much the better.

In the war between armed police and the unarmed public, the police should unilaterally disarm.