I recorded a 25-minute lecture on the COVID-19 economic crisis, with emphasis on increasing inequalities, for my Social Problems class. The slides are posted here, CC-BY.
Tag Archives: covid-19
A couple new ones and a couple updates.
The pandemic could be affecting the number of abortions, miscarriages, or infant deaths, but unless those effects are large it should be too early to see effects on the total birth rate, given that we’re only about 7 months into it here. So for possible birth indicators I did a little Google search analysis using the public Google Trends data.
I found three searches that were pretty well correlated in the weekly series: “am I pregnant”, “pregnancy test,” and “morning sickness”, which all should have something to do with the frequency of new pregnancies. I ran Google Trends back five years, created an index from these searches (alpha = .68) , smoothed it a little, and this is what I got:
There was already a big drop in 2019 from the previous three years (reasonable, based on recent trends), and then 2020 started out with a further drop. But then it spiked downward in March before rebounding back to its lower level. So, maybe that implies birth rates will keep falling but not off the charts compared with recent trends.
I also checked “missed period,” which was not well correlated with the others, and got this:
Again, 2019 was already showing some decline, and 2020 started out lower than that, and now searches for “missed period” are running lower than last year, but not more in the middle of the year than they were in the beginning. So, inconclusive for pandemic effect.
Here’s a new take on the Google trends for weddings. I took the averages of searches for “wedding invitations”, “wedding shower”, “bridal shower”, “wedding shower”, and “wedding dresses” (alpha=.94). With a little smoothing, here is 2020 compared with the average of the four previous years (unlike pregnancy searches, this one didn’t show a marked decline in 2019 compared with previous years).
March and April showed catastrophic declines in searches for wedding topics, and the rebound so far has been weak. However, weddings aren’t the same as marriages. Maybe people who had to cancel their weddings still got married down at whatever the pandemic equivalent of the courthouse is. So here’s the same analysis just for the search term “marriage license.” This shows a steep but not as catastrophic drop-off in March and April, and a stronger rebound. So maybe the decline in drop in marriages won’t be as big as the drop in weddings.
I previously showed the steep decline in recorded marriages in Florida. Here’s an update.
Florida lists recorded marriages by county and month, one month behind (see Table 17). They update as they go, so as of today August marriages are probably still not all recorded. The comparison with previous years shows a collapse in March and April, and then some rebounding. August is preliminary and will come up some.
Marriages in Florida normally peak between March and May. Of course it’s too early to say how many of these were just being postponed. The cumulative trend shows that through July Florida is down 24,000 marriages, or 27%, compared with last year.
When the going gets tough, the afflicted want to get divorced, but maybe they can’t. It’s expensive and time consuming and maybe people think it will upset the children even more. (I’ve written about divorce and recessions here and here). So my initial assumption going into the pandemic was that there would be a stall in divorces even though the intent to divorce would rise, followed by a rebound when people get a chance to act on their wishes.
Here I use Google search trends for four searches: “divorce lawyer”, “divorce attorney”, “get a divorce”, and “how to divorce”. The alpha for this index is .69 (when I just use the attorney and lawyer, the alpha is .86, but the result looks the same, so I’m showing the wider index). The results show a drop in divorce ideation in March into April, followed by a rebound to a level a little above the previous year average. Note this pandemic-spring drop is a lot less pronounced than the wedding and marriage collapses above.
Divorces take time, of course. Like births, I wouldn’t expect to see definitive results right away. In fact, it’s hard to know how long divorces are in process before they show up as recorded. However, in my favorite real-time demography state, Florida, they have been recording divorces every month, and have a look at this:
It’s a giant plunge in recorded divorces, almost 60% in April, followed by a weaker rebound. Again, the records are not yet complete, especially for August, so we’ll see. But comparing these patterns, it might be that there was a short suspension in divorce ideation as people were distracted by the crisis, followed by a rebound which hasn’t yet translated into divorce filings. Googling about divorce seems cheap and easy (and faster) compared with pulling it off, but this might mean there is growing pent up demand for divorce, which is bad (and may imply greater risks of conflict and violence).
Young adults living “at home”
I previous wrote about young adults living with their parents and grandparents using the June and then July Current Population Survey data made available by IPUMS.org. Subsequently, the Pew Research Center did something very similar using the data through July (with additional breakdowns and historical context). Pew used living with parents, apparently including those in households where the parents are not the householders. I prefer my definition — young adults living in the home of parents (also, or grandparents) — which fits better with the popular concept of living “at home.” So if your parents come to live with you, that’s different.
Anyway, here’s the update through August, which shows the percentage of young adults living at home falling back some from the June peak. I will be very interested to follow this through the fall.
Stata code for the living at home analysis is available here: https://osf.io/2xrhc/.
The pandemic and its attendant economic crisis is having massive effects on many aspects of family life. These early indicators are just possible targets of future analysis. There is a lot of other related work going on, which I’ve not taken the time to link to here. Please feel free to recommend other work in the comments.
Here’s the 2020 update of a series I started in 2013. This year, after the basic facts, I’ll add some pandemic facts below.
Is it true that “facts are useless in an emergency“? I guess we’ll find out this year. Knowing basic demographic facts, and how to do arithmetic, lets us ballpark the claims we are exposed to all the time. 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).
It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements like, “The U.S. economy lost a record 20.5 million jobs in April“?
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.
These 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 264 million and 396 million!).
This is only a few dozen facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). They 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 Population||7.7 billion||1|
|U.S. Population||330 million||1|
|Children under 18 as share of pop.||22%||2|
|Adults 65+ as share of pop.||17%||2|
|Official unemployment rate (July 2020)||10%||3|
|Unemployment rate range, 1970-2018||3.9% – 15%||3|
|Labor force participation rate, age 16+||61%||9|
|Labor force participation rate range, 1970-2017||60% – 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 pop||14%||2|
|Adults age 25+ with BA or higher||32%||2|
|Median household income||$60,300||2|
|Total poverty rate||12%||8|
|Child poverty rate||16%||8|
|Poverty rate age 65+||10%||8|
|Most populous country, China||1.4 billion||5|
|2nd most populous country, India||1.3 billion||5|
|3rd most populous country, USA||327 million||5|
|4th most populous country, Indonesia||261 million||5|
|5th most populous country, Brazil||207 million||5|
|U.S. male life expectancy at birth||76||6|
|U.S. female life expectancy at birth||81||6|
|Life expectancy range across countries||51 – 85||7|
|World total fertility rate||2.4||10|
|U.S. total fertility rate||1.7||10|
|Total fertility rate range across countries||1.0 – 6.9||10|
COVID-19 Addendum: 21 more facts
The pandemic is changing everything. A lot of the numbers above may look different next year. Here are 21 basic pandemic facts to keep in mind — again, the point is to get a sense of scale, to inform your consumption of the daily flow of information (and disinformation). These are changing, too, but they are current as of August 31, 2020.
Global confirmed COVID-19 cases: 25 million
Confirmed US COVID-19 cases: 6 million
Second most COVID-19 cases: Brazil, 3.9 million
Third most COVID-19 cases: India, 3.6 million
Global confirmed COVID-19 deaths: 850,000
Confirmed US COVID-19 deaths: 183,000
Second most COVID-19 deaths: Brazil, 121, 000
Third most COVID-19 deaths: India: 65,000
Percent of U.S. COVID patients who have died: 3%
COVID-19 deaths per 100,000 Americans: 50
COVID-19 deaths per 100,000 non-Hispanic Whites: 43
COVID-19 deaths per 100,000 Blacks: 81
COVID-19 deaths per 100,000 Hispanics: 55
COVID-19 deaths per 100,000 Americans over age 65: 400
Annual deaths in the U.S. (these are for 2017): Total, 2.8 million
Leading cause of death: Heart disease, 650,000
Second leading cause: Cancer: 600,000
Third leading cause: Accidents: 160,000
Deaths from flu and pneumonia, 56,000
Deaths from suicide: 47,000
Deaths from homicide: 20,000
COVID-19 country data: Johns Hopkins University Coronavirus Resource Center
U.S. cause of death data: Centers for Disease Control
U.S. age and race/ethnicity COVID-19 death data: Centers for Disease Control
Joanna Pepin was kind enough to interview me for her family sociology class (she’s just begun a new job at the University at Buffalo). We talked about why family sociology attracted me as an inequality researcher, what’s changed in modern families, some common misperceptions, what’s new the forthcoming edition of my textbook, and what COVID-19 is likely to mean for people and their families. In 11 minutes.
I hope it helps.
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.
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.
* The IPUMS codebook, Stata code, spreadsheet, and figures are in an Open Science Framework project under CC0 license here: osf.io/2xrhc.
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.
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
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.
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.)
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.
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?
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.