My green screen teaching setup explained

Janine Barchas, a professor of English who sells advice on “curating your material environment and adjusting the visible setting of your at-home office” for $250 per chat, managed to place a (paywalled) article in the Chronicle of Higher Education, which I haven’t read. But I did see people complaining on twitter about her advice that you “should curate your zoom backdrop.” Including this funny spoof from Andrew Ishak:

There was other followup advice, like this:

If you are white and male enough to own an expensive, new, and highly performing computer, you can opt for a virtual background. Several colleagues poignantly use photos of their now-vacant classrooms or offices. But not everyone has an up-to-date computer. Even for those who do, hours of flickering like a TV weather announcer in front of a greenscreen projection of the Grand Canyon or of your college campus can prove distracting, too. You might consider selling some of your Apple stock to purchase a top of the line machine, but only if you make sure to mention its purchase at the start of every meeting. After all, what use is having expensive things if you can’t constantly bring them up to others?

(I don’t know who wrote that, but it was shared here.)

All that said, I spend hours and hours in online video meetings, and I’m preparing to teach for hours and hours on Zoom. I want to feel like I’m doing a good job, and also maybe enjoy my job a little. I don’t want to decorate my living space to show students and colleagues in the background, I want a nice green screen setup to put me somewhere else. With under $300 and 4 x 6 feet of space, I found this was possible.

So, without telling anyone what they should do, or even implying that they should do something, this is a 4-minute explanation of how I got to be satisfied, on the very relative scale of our current “situation,” with my Zoom self for teaching. If it’s helpful, great. If you get pleasure from mocking me for it, you’re welcome.

Good luck this semester!

Families, inequality, and sociology in pandemic times (video)

This fall I will be recording video lectures for students in my undergrad class. I’m thinking about the technical aspects, but also the voice and posture. Sitting at my desk at home is quite different from my lecture hall (I usually get a few thousand steps during an hour class). We’ll have to see how it goes.

In June I had a chance to do a one-hour consulting with a “major corporation” to talk about what’s happening in the world, which I recorded and rewrote into this post. I just did another one on the subject of modern families and inequality. This one was like an interview, where I answered questions. I transcribed some of my answers, and then edited that text, figuring it might give me a nice blend of formal and conversational voice, which might work in a video.

After recording the video, I went back and added in some graphics using Photoshop as my video editor (did you know we can get Photoshop as part of our university site license?). A much quicker and easier way, which I assume I’ll be reduced to in the fall, is just to record the lecture live using Zoom or some other PowerPoint screen recorder. Anyway, here is the result, in 12 minutes.

Note: The video includes an update to data from this post on weddings in Florida, and this report on the impact of the epidemic on reproductive health experiences, from Laura Lindberg and colleagues at Guttmacher.

Why there are 3.1 million extra young adults living at home

Answer: The COVID-19 pandemic.

UPDATE: A new post updates this analysis for July 2020

Catherine Rampell tweeted a link to a Zillow analysis showing 2.2 million adults ages 18-25 moving in with their parents or grandparents in March and April. Zillow’s Treh Manhertz estimates these move-homers would cost the rental market the better part of a billion dollars, or 1.4% of total rent if they stay home for a year.

We now have the June Current Population Survey data to work with, so I extended this forward, and did it differently. CPS is the large, monthly survey that the Census Bureau conducts for the Bureau of Labor Statistics each month, principally to track labor market trends. It also includes basic demographics and living arrangement information. Here is what I came up with.*

Among people ages 18-29, there is a large spike of living in the home of a parent or grandparent (of themselves or their spouse), which I’ll call “living at home” for short. This is apparent in a figure that compares 2020 with the previous 5 years (click figures to enlarge):

From February to April, the percentage of young adults living at home jumped from 43% to 48%, and then up to 49% in June. Clearly, this is anomalous. (I ran it back to 2008 just to make sure there were no similar jumps around the time of the last recession; in earlier years the rates were lower and there were no similar spikes.) This is a very large disturbance in the Force of Family Demography.

To get a better sense of the magnitude of this event, I modeled it by age, sex, and race/ethnicity. Here are the estimated share of adults living at home by age and sex. For this I use just June of each year, and compare 2020 with the pooled set of 2017-2019. This controls for race/ethnicity.

The biggest increase is among 21-year-old men and 20-year-old women, and women under 22 generally. These may be people coming home from college, losing their jobs or apartments, canceling their weddings, or coming home to help.

I ran the same models but broke out race/ethnicity instead (for just White, Black, and Latino, as the samples get small).

This shows that the 2020 bounce is greatest for Black young adults (below age 24) and the levels are lowest for Latinos (remember that many Latinos are immigrants whose parents and grandparents don’t live in the US).

To show the total race/ethnic and gender pattern, here are the predicted levels of living at home, controlling for age:

The biggest 2020 bounce is among Black men and women, with Black men having the highest overall levels, 58%, and White women having the lowest at 44%.

In conclusion, millions of young adults are living with their parents and grandparents who would not be if 2020 were like previous years. The effect is most pronounced among Black young adults. Future research will have to determine which of the many possible disruptions to their lives is driving this event.

For scale, there are 51 million (non-institutionalized) adults ages 18-29 in the country. If 2020 was like the previous three years, I would expect there to be 22.2 million of them living with their parents. Instead there are 25.4 million living at home, an increase of 3.1 million from the expected number (numbers updated for June 2020). That is a lot of rent not being spent, but even with that cost savings I don’t think this is good news.

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* The IPUMS codebook, Stata code, spreadsheet, and figures are in an Open Science Framework project under CC0 license here: osf.io/2xrhc.

Framing social class with sample selection

A lot of qualitative sociology makes comparisons across social class categories. Many researchers build class into their research designs by selecting subjects using broad criteria, most often education level, income level, or occupation. Depending on the set of questions at hand, the class selection categories will vary, focusing on, for example, upbringing and socialization, access to resources, or occupational outlook.

In the absence of a substantive review, here are a few arbitrarily selected examplar books from my areas of research:

• Mothering While Black: Boundaries and Burdens of Middle-Class Parenthood, by Dawn Marie Dow
• The Tumbleweed Society: Working and Caring in an Age of Insecurity, by Alison Pugh [my review]
• Negotiating Opportunities: How the Middle Class Secures Advantages in School, by Jessica McCrory Calarco.
• For the Family?: How Class and Gender Shape Women’s Work, by Sarah Damaske

This post was inspired by the question Caitlyn Collins asked the other day on Twitter:

#soctwitter, I'd love thoughts on how to operationalize social class categories for sampling in qualitative research. Yes, multidimensional evals with income, ed, occupation.. but I want best practices for actual schema to be used as filters for fieldwork, w people, face to face.

— Caitlyn Collins (@CaitlynMCollins) June 27, 2020

She followed up by saying, “Social class is nebulous, but precision here matters to make meaningful claims. What do we mean when we say we’re talking to poor, working class, middle class, wealthy folks? I’m looking for specific demographic questions, categories, scales sociologists use as screeners.” The thread generated a lot of good ideas.

Income, education, occupation

Screening people for research can be costly and time consuming, so you want to maximize simplicity as well as clarity. So here’s a way of looking at some common screening variables, and what you might get or lose by relying on them in different combinations. This uses the 2018 American Community Survey, provided by IPUMS.org (Stata data file and code here).

• I used income, education, and occupation to identify the status of individuals, and generated household class categories by the presence of absence of types of people in each. That means everyone in each household is in the same class category (a choice you might or might not want to make).
• Income: Total household income divided by an equivalency scale (for cost of living). The scale counts each adult as 1 person, each child under 18 as .70, and then scales that count by ^.70. I divided the resulting distribution into thirds, so households are in the top, middle, or bottom third. Top third is what I called “middle/upper” class, bottom third is “lower class.”
• Education: I use BA degree to identify households that have (middle/upper) or don’t (lower) a four-year college graduate present. This is 31% of adults.
• Occupation: I used the 2018 ACS occupation codes, and coded people as middle/upper class if their codes was 10 to 3550, which are management, business, and financial occupations; computer, engineering, and science occupations; education, legal, community service, arts, and media occupations; and healthcare practitioners and technical occupations. It’s pretty close to what we used to call “managerial and professional” occupations. Together, these account for 37% of workers.

So each of these three variables identifies an upper/middle class status of about a third of people.

For lower class status, you can just reverse them. The except is income, which is in three categories. For that, I counted households as lower class if their household income was in the bottom third of the adjusted distribution. In the figures below, that means they’re neither middle/upper class nor lower class if they’re in the middle of the income distribution. This is easily adjusted.

Venn diagrams

You can make Venn diagrams in Stata using the pvenn2 add-on, which I naturally discovered after making these. If  you must know, made these by generating tables in Stata, downloading this free plotter app, entering the values manually, copying the resulting figures into Powerpoint and applying the text there, then printing them to PDF, and extracting the images from PDF using Photoshop. Not recommended workflow.

Here they are. I hope the visuals might help people think about for example, who they might get if they screened on just one of these variables, or how unusual someone is who has a high income or occupation but no BA, and so on. But draw your own conclusions (and feel free to modify the code and follow your own approach). Click to enlarge.

First middle/upper class:

Then lower class:

I said draw your own conclusions, but please don’t draw the conclusion that I think this is the best way to define social class. That’s a whole different question. This is just about simply ways to select people to be research subjects. For other posts on social class, follow this tag, which includes this post about class self identification by income and race/ethnicity.

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Data and code: osf.io/w2yvf/

Where preprints fit in, COVID-19 edition

I recorded a 16-minute talk on the scientific process, science communication, and how preprints fit in to the information ecosystem around COVID-19.

It’s called, “How we know: COVID-19, preprints, and the information ecosystem.” The video is on YouTube here, also embedded below, and the slides, with references, are up here.

Happy to have your feedback, in the comments or any other way.

What happens next

Wouldn’t you like to know.

“The pandemic has exposed the messiness of science. … We all want answers today, and science is not going to give them. … Science is uncertainty. And the pace of uncertainty reduction in science is way slower than the pace of a pandemic.” —Brian Nosek

click to enlarge

The math of probability is manageable, up to a point. In principle, you could calculate the odds of, for example (clockwise from top left) getting heads on the fake Trump coin, a novel coronavirus linking up to human proteins, surviving a round of Russian roulette, having someone with COVID-19 at your planned event, rolling 6-6-4-8-20 on the dice, have all the marbles fall under a normal curve on a Galton board, and then surviving a flight from New York to Los Angeles without hitting one of the thousands of other planes in the air. But that doesn’t mean we can tell where humanity will be a year from now.

Of course things are always too complicated to predict perfectly, but “normally” we bracket uncertainty and make simplifying assumptions so we can work with forecasts of say, tomorrow’s weather or next quarter’s economic growth — which are strongly bounded by past experience. These are “the models” we live by. The problem now is not that reality has become objectively harder to predict, it’s that the uncertainties in the exercise (those most relevant to our lives) involve events with such catastrophic consequences that a normal level of uncertainty now includes outcomes so extreme that we can’t process them meaningfully.

Once nominally predicable events start influencing each other in complex ways, the uncertainty grows beyond the capacity of simple math. Instead of crunching every possibility, we simplify the assumptions, based on past experience and the outcomes we consider possible. Today’s would-be predictions, however, involve giant centrifugal forces, so that small deviations can lead to disintegration. For example, if the pandemic further tanks the economy, which provides more unemployed people to populate mass protests, leading to more military crackdown and turning more people against Trump, it might make him lash out more at China, and then they might not share their vaccine with us, and an epidemic wave could overwhelm the election and its aftermath, giving Trump a pretext for nullifying the results. And so on.

To make matters more ungraspable, we personally want to know what’s going on at the intersection of micro and macro forces, where we don’t have the data to use even if we knew what to do with it.

Examples

For example, as individuals try to ballbark their own risks of covid-19 infection, and the likelihood of a serious outcome in that event — given their own health history — they might also want to consider whether they have been exposed to tear gas at the hands of police or the military, which might increase the chance of infection. In that case, both the individual and the state are acting without quantifiable information on the risks. For another example, Black people in America obviously have a reasonable fear of police violence — with potentially immeasurable consequences — but taking the risk to participate in protests might contribute to political changes that end up reducing that risk (for themselves and their loved ones). The personal risks are affected by policy decisions and organizational practices, but you can’t predict (much less control) the outcomes.

Individual risks are affected by group positions, of course, creating diverging profiles that splinter out to the individual level. Here’s an example: race and widowhood. We all know that as a married couple ages, the chance that one of the partners will die increases. But that relationship between age and widowhood differs markedly between Blacks and Whites, as this figure shows:

Before age 70, the annual probability of a Black woman being widowed is more than twice the chance that White women face. (After that, the odds are higher, but not as dramatically so.) Is this difference big enough to affect people’s decision making, their emotions, their relationships? I think so, though I can’t prove it. Even if people don’t map out the calculation at this level (though they of course think about their own and their partner’s specific health situation), it’s in there somewhere.

For most people, widowhood presents a pretty low annual probability of a very bad event, one that might turn your life upside down. On the other hand, climate change is certain, and observable over the course of a contemporary adult’s lifetime (look at the figure below, from 1980 to 2020). But although climate change presents potentially catastrophic consequences, the risks aren’t easily incorporated into life choices. If you’re lucky, you might have to think about the pros and cons of owning beachfront property. Or you could be losing a coal job, or gaining a windmill job. But I think for most people in the U.S. it’s in the category of background risk — which might motivate political participation, for example, but doesn’t hang over one’s head as a sense of life-threatening risk.

If not imminent fear, however, climate change undoubtedly contributes to a climate of uncertainty about the future. Interestingly, there is a robust debate about whether and how climate change is also increasing climate variability. Rising temperatures alone would create more bad storms, floods, and droughts, but more temperature fluctuation would also have additional consequences. I was interested to read this paper which showed models predicting greater change in temperature variability (on the y-axis) for the rest of the century in countries with lower per-capita income (x-axis). When it comes to inequality, it rains and it pours. And for people in poorer countries of the world, it’s raining uncertainty.

What comes next

I wrote about unequal uncertainties in April, and possible impacts on marriage rates, and I’ve commented elsewhere on fertility and family violence. But I’m not making a lot of predictions. Are other social scientists? My impression they’re mostly wisely holding off. My sense is also that this may be part of a longer-term pattern, where social scientists once made more definitive predictions with less sophisticated models than we do now that we’re buried in data. Is it the abundance of data that makes predicting seem like a bad business? I don’t think that’s it. I think it’s the diminished general confidence in the overall direction of social change. Or maybe predictions have just become more narrow — less world revolution and more fourth quarter corn prices.

One of the books I haven’t written yet, crappily titled Craptastic when I pitched it in 2017, would address this:

My theory for Craptastic is that the catastrophic thinking and uncontrollable feelings of impending doom go beyond the very reasonable reaction to the Trump shitshow that any concerned person would have, and reflect a sense that things are turning around in a suddenly serious way, rupturing what Anthony Giddens describes as the progress narratives of modernity people use to organize their identities. People thought things were sort of going to keep getting better, arc of the moral universe and all that, but suddenly they realize what a naive fantasy that was. It’s not just terrible, it’s craptastic. …

I suspect that if America lives to see this chapter of its decline written, Trump will not be as big a part of the story as it seems he is right now. And that impending realization is one reason for the Trump-inspired dysphoria that so many people are feeling.

Social science is unlikely anytime soon to be the source of reassurance about the future some people might be looking for — not even the reassurance that things will get better, but just confidence that we know what direction we’re headed, and at what speed. I don’t know, but if you know, feel free to leave it in the comments. (Which are moderated.)

Rural COVID-19 paper peer reviewed. OK?

Twelve days ago I posted my paper on the COVID-19 epidemic in rural US counties. I put it on the blog, and on the SocArXiv paper server. At this writing the blog post has been shared on Facebook 69 times, the paper has been downloaded 149 times, and tweeted about by a handful of people. No one has told me it’s wrong yet, but not one has formally endorsed it yet, either.

Until now, that is. The paper, which I then submitted to the European Journal of Environment and Public Health, has now been peer reviewed and accepted. I’ve updated the SocArXiv version to the journal page proofs. Satisfied?

It’s a good question. We’ll come back to it.

Preprints

The other day (I think, not good at counting days anymore) a group of scholars published — or should I say posted — a paper titled, “Preprinting a pandemic: the role of preprints in the COVID-19 pandemic,” which reported that there have already been 16,000 scientific articles published about COVID-19, of which 6,000 were posted on preprint servers. That is, they weren’t peer-reviewed before being shared with the research community and the public. Some of these preprints are great and important, some are wrong and terrible, some are pretty rough, and some just aren’t important. This figure from the paper shows the preprint explosion:

All this rapid scientific response to a worldwide crisis is extremely heartening. You can see the little sliver that SocArXiv (which I direct) represents in all that — about 100 papers so far (this link takes you to a search for the covid-19 tag), on subjects ranging from political attitudes to mortality rates to traffic patterns, from many countries around the world. I’m thrilled to be contributing to that, and really enjoy my shifts on the moderation desk these days.

On the other hand some bad papers have gotten out there. Most notoriously, an erroneous paper comparing COVID-19 to HIV stoked conspiracy theories that the virus was deliberately created by evil scientists. It was quickly “withdrawn,” meaning no longer endorsed by the authors, but it remains available to read. More subtly, a study (by more famous researchers) done in Santa Clara County, California, claimed to find a very high rate of infection in the general population, implying COVID-19 has a very low death rate (good news!), but it was riddled with design and execution errors (oh well), and accusations of bias and corruption. And some others.

Less remarked upon has been the widespread reporting by major news organizations on preprints that aren’t as controversial but have become part of the knowledge base of the crisis. For example, the New York Times ran a report on this preprint on page 1, under the headline, “Lockdown Delays Cost at Least 36,000 Lives, Data Show” (which looks reasonable in my opinion, although the interpretation is debatable), and the Washington Post led with, “U.S. Deaths Soared in Early Weeks of Pandemic, Far Exceeding Number Attributed to Covid-19,” based on this preprint. These media organizations offer a kind of endorsement, too. How could you not find this credible?

Peer review

To help sort out the veracity or truthiness of rapid publications, the administrators of the bioRxiv and medRxiv preprint servers (who are working together) have added this disclaimer in red to the top of their pages:

Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

That’s reasonable. You don’t want people jumping the gun on clinical decisions, or news reports. Unless they should, of course. And, on the other hand, lots of peer reviewed research is wrong, too. I’m not compiling examples of this, but you can always consult the Retraction Watch database, which, for example, lists 130 papers published in Elsevier journals in 2019 that have been retracted for reasons ranging from plagiarism to “fake peer review” to forged authorship to simple errors. The database lists a few peer-reviewed COVID-19 papers that have already been retracted as well.

This comparison suggests that the standard of truthiness cannot be down to the simple dichotomy of peer reviewed or not. We need signals, but they don’t have to be that crude. In real life, we use a variety of signals for credibility that help determine how much to trust a piece of research. These include:

• The reputation of the authors (their degrees, awards, twitter following, media presence)
• The institutions that employ them (everyone loves to refer to these when they are fancy universities reporting results they favor, e.g., “the Columbia study showed…”)
• Who published it (a journal, an association, a book publisher), which implies a whole secondary layer of endorsements (e.g., the editor of the journal, the assumed expertise of the reviewers, the prestige or impact factor of the journal as a whole, etc.)
• Perceived conflicts of interest among the authors or publishers
• The transparency of the research (e.g., are the data and materials available for inspection and replication)
• Informal endorsements, from, e.g., people we respect on social media, or people using the Plaudit button (which is great and you should definitely use if you’re a researcher)
• And finally, of course, our own assessment of the quality of the work, if it’s something we believe ourselves qualified to assess

As with the debate over the SAT/GRE for admissions, the quiet indicators sometimes do a lot of the work. Call something a “Harvard study” or a “New York Times report,” and people don’t often pry into the details of the peer review process.

Analogy: People who want to eat only kosher food need something to go on in daily life, and so they have erected a set of institutional devices that deliver such a seal (in fact, there are competing seal brands, but they all offer the same service: a yes/no endorsement by an organization one decides to trust). The seals cost money, which is added to the cost of the food; if people like it, they’re willing to pay. But, as God would presumably tell you, the seal should not always substitute for your own good judgment because even rabbis or honest food producers can make mistakes. And in the absence of a good kosher inspection to rely on altogether, you still have to eat — you just have to reason things through to the best of your ability. (In a pinch, maybe follow the guy with the big hat and see what he eats.) Finally, crucially for the analogy, anyone who tells you to ignore the evidence before you and always trust the authority that’s selling the dichotomous indicator is probably serving their own interests as least as much as they’re serving yours.

In the case of peer review, giant corporations, major institutions, and millions of careers depend on people believing that peer review is what you need to decide what to trust. And they also happen to be selling peer review services.

My COVID-19 paper

So should you trust my paper? Looking back at our list, you can see that I have degrees and some minor awards, some previous publications, some twitter followers, and some journalists who trust me. I work at a public research university that has its own reputation to protect. I have no apparent way of profiting from you believing one thing or another about COVID-19 in rural areas (I declared no conflicts of interest on the SocArXiv submission form). I made my data and code available (even if no one checks it, the fact that it’s there should increase your confidence). And of course you can read it.

And then I submitted it to the European Journal of Environment and Public Health, which, after peer review, endorsed its quality and agreed to publish it. The journal is published by Veritas Publications in the UK with the support of Tsinghua University in China. It’s an open access journal that has been publishing for only three years. It’s not indexed by Web of Science or listed in the Directory of Open Access Journals. It is, in short, a low-status journal. On the plus side, it has an editorial board of real researchers, albeit mostly at lower status institutions. It publishes real papers, and (at least for now) it doesn’t charge authors any  publication fee, it does a little peer review, and it is fast. My paper was accepted in four days with essentially no revisions, after one reviewer read it (based on the summary, I believe they did read it). It’s open access, and I kept my copyright. I chose it partly because one of the papers I found on Google Scholar during my literature search was published there and it seemed OK.

So, now it’s peer reviewed.

Here’s a lesson: when you set a dichotomous standard like peer-reviewed yes/no and tell the public to trust it, you create the incentive for people to do the least they can to just barely get over that bar. This is why we have a giant industry of tens of thousands of academic journals producing products all branded as peer reviewed. Half a century ago, some academics declared themselves the gatekeepers of quality, and called their system peer review. To protect the authority of their expertise (and probably because they believed they knew best), they insisted it was the standard that mattered. But they couldn’t prevent other people from doing it, too. And so we have a constant struggle over what gets to be counted, and an effort to disqualify some journals with labels like “predatory,” even though it’s the billion-dollar corporations at the top of this system that are preying on us the most (along with lots of smaller scam purveyors).

In the case of my paper, I wouldn’t tell you to trust it much more because it’s in EJEPH, although I don’t think the journal is a scam. It’s just one indicator. But I can say it’s peer reviewed now and you can’t stop me.

Aside on service and reciprocity: Immediately after I submitted my paper, the EJEPH editors sent me a paper to review, which I respect. I declined because it wasn’t qualified, and then they sent me another. This assignment I accepted. The paper was definitely outside my areas of expertise, but it was a small study quite transparently done, in Nigeria. I was able to verify important details — like the relevance of the question asked (from cited literature), the nature of the study site (from Google maps and directories), the standards of measurement used (from other studies), the type of the instruments used (widely available), and the statistical analysis. I suggested some improvements to the contextualization of the write-up and recommended publication. I see no reason why this paper shouldn’t be published with the peer review seal of approval. If it turns out to be important, great. If not, fine. Like my paper, honestly. I have to say, it was a refreshing peer review experience on both ends.

Graduation remarks, 2020 edition: ‘We need you.’

Graduations are online this year. The good news is you can shop around for whatever speeches they want (Your choice: Barack Obama or Melania Trump). If you want one that’s under 5 minutes, with a 75/25 dark-uplifting ratio, aging leftist sensibility, and a little sociology, here’s the text:

 

Congratulations to the students graduating this year. You deserve to be congratulated for your accomplishments and the accomplishments of your family and community members as much as any other graduating class in history. Congratulations.

If that’s all you wanted to hear you can turn it off now. I won’t begrudge you. Because what’s next is going to be dark.

It’s common in graduation speeches to tell the promising graduates that the future is in your hands. That you will determine the course of our history in the future. I hope that is true. I sincerely hope that’s true. But I can’t promise you that, and neither can anyone else. Because we don’t know what’s going to happen next.

Humanity has gone through and is still going through a tragedy of unspeakable proportions. Millions of people have been sickened, many have suffered horribly, and hundreds of thousands have died, in the pandemic. And everyone has been disrupted, personally, economically, socially.

This virus travels the world on the backs of the healthy to infect and kill – among others – the old and the weak. The devastation has been worse in the United States than anywhere else, because of our systemic weaknesses, but now that it has set its sights on the poorer countries of the world, that likely won’t be true for long.

And that’s not the extent of our problems, of course. We’re in this predicament now because “normal” was already not on a sustainable path. Trump and the racist, nationalist horse he rode in on, the obscene concentration of wealth, climate change, guns, segregation, xenophobia, sexual violence, the degradation of our infrastructure, including science and science education, were all setting us up for this moment. Even if we can contain this pandemic, there is no sustainable normal to get back to.

And our tools for responding may not be up to the task. Our democracy is frail. Our discourse is polluted. Social media generates ever-expanding spirals of polarization, and it has displaced many of our other communication tools. Like journalism.

This pandemic will bring out more bad things. It will exacerbate inequality. It will lead people to shut down, and shut in, fear others, blame others. It has already put a damper on travel and social exchange across all kinds of boundaries, which has been a force for good – and that might last for a long time. And more people will suffer and die, many unnecessarily.

It could make bad things worse, if the economic crisis is long and deep, xenophobia rises, conflict flares up, war, political paralysis. No one can tell you these things are not very real possibilities.

But. In the contours of this crisis we can also see how to begin to make things better, how we could turn things around. If we make it possible, we could recognize the importance of collective action for global problems, including public health but also climate change. We can learn the importance of science and education. We can see the value of investing in social and material infrastructure, including the tools for public health. We might even learn the usefulness of government for saving us from the threats we face.

And you – You can still have great lives. Happy and productive and kind and generous and adventurous and doing the best you can. Which is what people have always hoped for. And you can do those things even if you can’t turn this all around. Look, people have made good lives in hard times before. You make life worth living by what you put into it, which is no more true in good times than bad.

And, we do need you. Even if you’re not not ready to invent this vaccine or fix our broken government. I hope you have your chance to do things like that, too. But before that we need you to figure out how we live with purpose and perspective. How we avoid turning inward and shutting down even as the physical distances between us grow. How to pull down barriers within our own walls. I hope you’ll help us, and yourselves, and the generations to come, figure this out.

Thank you. Good luck.

The COVID-19 epidemic in rural U.S. counties

I’ve been working on the COVID-19 epidemic in rural U.S. counties, and have now posted a paper on SocArXiv, here: https://osf.io/preprints/socarxiv/pnqrd/. Here’s the abstract, then some figures below:

Having first reached epidemic proportions in coastal metropolitan areas, COVID-19 has spread around the country. Reported case rates vary across counties from zero to 125 per thousand population (around a state prison in the rural county of Trousdale, Tennessee). Overall, rural counties are underrepresented relative to their share of the population, but a growing proportion of all daily cases and deaths have been reported in rural counties. This analysis uses daily reports for all counties to present the trends and distribution of COVID-19 cases and deaths in rural counties, from late March to May 16, 2020. I describe the relationship between population density and case rates in rural and non-rural counties. Then I focus on noteworthy outbreaks linked to prisons, meat and poultry plants, and nursing homes, many of which are linked to high concentrations of Hispanic, American Indian, and Black populations. The growing epidemic in rural counties is apparently driven by outbreaks concentrated in these institutional settings, which are conducive to transmission. The impact of the epidemic in rural areas may be heightening due to their weaker health infrastructure and more vulnerable populations, especially due to age, socioeconomic status, and health conditions. As a result, the epidemic may contribute to the ongoing decline of health, economic, and social conditions in rural areas.

Here are COVID-19 cases in rural counties across the country. Note that the South, Mid-Atlantic, Michigan, and New England have the most (fewer in West and upper Midwest). When you look at cases per capita, you see the concentration in the South and isolated others.

COVID is still underrepresented in rural counties, but their share of the national burden is increasing, as they keep adding more than 2,000 cases and just under 100 deaths per day.

Transmission dynamics are different in rural counties. They show a weaker relationship between pop density and cases. This suggests to me that there are more idiosyncratic factors at work (prisons, meat plants, nursing homes), which are high concentrations of vulnerable people.

These are the rural outbreak cases I identified, for which I could find obvious epidemic centers in institutions: Prisons, meatpacking and poultry plants, and nursing homes. These 28 select counties account for 15% of the rural burden.

In addition to the institutional concentration, these outbreak cases also show distinct overrepresentation of Hispanic, American Indian, and Black populations. Here are some of the outbreak cases plotted against minority concentrations.

And here’s a table of those selected cases:

Lots more to be done, obviously. It’s a strong limitation to be restricted to case and death counts at the county level. Someone could go get lists of prisons and meatpacking plants and nursing homes and run them through this, etc. But I wanted to raise this issue substantively. By posting the paper on SocArXiv, without peer review, I’m offering it up for comment and criticism. Also, I’m sharing the code (which links to the data, all public): https://osf.io/wd2n6/. Messy but usable.

A related thought on writing a paper about COVID19 right now: The lit review is daunting. There are thousands of papers, most on preprint servers. Is this bad? No. I use various tools to decide what’s reliable to learn from. If it’s outside my area, I’m more likely to rely on peer-reviewed journals, or those that are widely citied or reported. But the vast quantity available still helps me see what people are working on, what terms, and types of data they use. I learned a tremendous amount. Much respect to the thousands of researchers who are doing what they can to respond to this global crisis.

How big will the drop in weddings be? Big

With data snapshot addendum at the end.

In the short run, people are canceling their weddings that were already booked, or not planning the ones they were going to have this summer or fall. In the long run, we don’t know.

To look at the short run effect, I used Google Trends to extract the level of traffic for five searches over the last five years: wedding dresses, bridal shower, wedding license, wedding shower, and wedding invitations (here is the link to one, just change the terms to get the others). These are things you Google when you’re getting married. Google reports search volume for each term weekly, scaled from 0 to 100.

Search traffic for these terms is highly correlated with each other across weeks, between .45 and .76. I used Stata to combine them into an index (alpha = .92), which ranges from 22 to 87 for 261 weeks, from May 2015 to last week.

For the graph, I smoothed the trend with a 5-week average. Here is the trend, with dates for the peaks and troughs (click to enlarge):

The annual pattern is very strong. Each year people people do a lot of wedding searches for about two months, from mid-January to early-March, before traffic falls for the rest of year, until after Thanksgiving. There is a decline over these five years, but I don’t put too much stock in that because maybe the terms people use are changing over time.

But this year there is a break. After starting out with a normal spike in mid-January, searches lurched downward into February, and then collapsed to their lowest level in five years — at what should have been the height of the wedding Google search season.

Clearly, there will be a decline in weddings this spring and summer, or until we “reopen,” whatever that means. A lot of people just can’t get married. When you think about the timing of marriage, most people getting married in a given year are probably already planning to at least half a year in advance. So even if no one’s relationships are affected, and their long term plans don’t change, we’ll still see a decline in marriages this year just from canceled plans.

Beyond that, however, people probably aren’t meeting and falling in love as much. People who are dating probably aren’t as likely to advance their relationships through what would have been a normal development – dating, love, kids, marriage, and so on. So a lot of existing relationships – even for people who weren’t engaged – probably aren’t moving toward marriage. Even if they get back on track later, that’s a delay of a year or two or however long. This says nothing about people being stressed, miserable, sick (or worse), and otherwise not in any kind of mood.

In the longer term, what does the pandemic mean for confidence in the future? The crisis will undermine people’s ability to make long term decisions and commitments. Unless the cultural or cognitive model of marriage changes, insecurity or instability will mean less marriage in the future. This could be a long term effect even after the acute period passes.

What about a rebound? Eventually – again, whenever that is – there probably will be some rebound. At least, just practically, some people who put off marriage will go ahead and do it later. Although, as with delayed births, some postponed marriages probably will end up being foregone. On a larger scale, when people can get out and get together and get married again, there might well be a marriage bounce (and also even a baby boom). Presumably that would depend on a very positive scenario: a vaccine, an economic resurgence, maybe a big government boost, like after WWII. A surge in optimism about the future, happiness. That’s all possible. This also depends on the cultural model of marriage we have now, so that good times equals more marriage (and childbearing). In real life, any such effect might be small, dwarfed by big declines from chaos, fear, and uncertainty. I can’t predict how these different impulses might play against each other. However, on balance, my out-on-a-limb forecast is a decline in marriage.

Data snapshot addendum

I didn’t realize there was monthly data available already. For example, in Florida they release monthly marriage counts by county, and they have released the April numbers. These show a 1% increase in marriages year-over-year in January, a 31% increase in February, then a 31% drop in March and a 72% drop in April [Since I first posted this, Florida added 477 more marriages in April, and a few in the earlier months, changing these percentages by a couple points as on June 5. -pnc.] Here is a scatter plot [updated] showing the count of marriages by county in 2019 and 2020. Counties below the diagonal have fewer marriages in 2020 than they did in 2019. Not surprising, but still dramatic to see it happening in “real time” (not really, just in quickly available data).