https://www.forbes.com/sites/vickiphillips/2023/01/24/gen-z-hopeless-or-hopeful/?sh=6034de5e10f9

Gen Z: Hopeless Or Hopeful?

Vicki PhillipsJan 24, 2023,

I cover how education prepares students to become solution seekers.

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Gen Z is actively building movements for change to transform our world and its future for the ... [+]

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Is Gen Z the most "depressed, anxious, and fragile" generation ever?

That's the thesis of research by Jonathan Haidt, a social psychologist at New York University's Stern School of Business.

Haidt, the author of "The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure" and two forthcoming volumes with similarly doom and gloom titles, told the Wall Street Journal's Tunku Varadarajan that this supposed fragility of Gen Z is a "national crisis" that will imperil American capitalism, culture and social cohesion.

Reading Varadarajan's piece, you could be forgiven for thinking that Haidt hasn't stepped foot outside of his "professorial…book-lined" office to connect with the lives, experiences and aspirations of actual Gen-Zers.

Haidt's conjecture of the impending downfall of our society because of "kids these days'' is nothing new. Indeed the younger generation is seemingly always the target of the barbs of such serious-people-who-study-serious-matters. Look no further than the recent thread by political scientist Paul Fairie who found that every generation going back to the 1920's has been called "too soft" by the Haidts of their era. It wasn't so long ago that millennials were lamented as hopelessly entitled, self-centered, and lazy. In time, their contributions and potential to improve the world came to be rightly acknowledged.

What Haidt does get right is that Gen Z has faced unprecedented challenges and novel social dynamics. This is a generation who is coming of age in an era of mass shootings in schools, a global pandemic, and turbocharged political turmoil. These things will invariably take their toll on young people, and all people. What he gets flat wrong is how young people are responding. The truth is that they are not just effectively navigating those challenges, but are actively building movements for change to transform our world and its future for the better. Gen Z is perseverance personified.

Haidt follows the same tired declension narrative that his rhetorical forebearers did. He builds his generation's version in part on the increase of reported incidents of depression and anxiety among Gen-Zers, which he attributes to overbearing parenting and too much time on social media. He fails to note the remarkable truth. Gen Z is in fact embracing a new, more open and honest relationship with their mental health, one that destigmatizes the issue so that it can be addressed. This is leading to more people reporting their mental health challenges and seeking support, and contributing to the rising numbers of reported cases. More effective diagnoses and increased connection to care are both good by any measure.

It's really a matter of perspective. Haidt sees Gen Z as hopelessly lost in their screens, drowning in the pursuit of digital affirmation through social media. He ignores how young people are actually leveraging these technologies to solve the intractable problems that older generations have proven unable or unwilling to address. It is this generation that is taking the lead on issues like food scarcity, climate change, and racial injustice.

Gen Z is drinking less, learning more, and embracing a spirit of global agency and impact that prior generations could not even imagine. Which raises the question: what were later Boomers and Gen-Xers of Haidt's cohort doing when they were 15, 16 and 17?

While social media has certainly fomented the spread of conspiracy theories, movements to undermine democratic institutions, and political radicalization writ large, these have largely been the machinations of generations other than Zers.

Instead, Gen Z was using that technology to register voters, win elections, and elect their peers to local, state and federal offices. But Haidt shrugs off that reality and the contributions of Gen Z luminaries like Greta Thunberg and Malala Yousafza by arguing they're no Mark Zuckerbergs. Apparently missing the irony of elevating the person most responsible for the rise of social media while also bemoaning the effects of his creations.

As an educator, I feel an obligation to support Haidt on his personal learning journey. And as someone who has actually worked with Gen-Zers, I can tell you, the kids these days are more than alright.

Take Joe Nail, who in his early 20s founded Lead For America, a national service program to build a new generation of leaders. Joe and his team are keeping talented young people in their hometowns to strengthen their communities. Lead for America provides fellowships to support these emerging leaders and prevent these urban neighborhoods, small towns and rural areas from the brain drain that can undermine local economies and social vibrancy.

Or Andrew Brennen who cofounded a youth organization in his home state of Kentucky that successfully lobbied the state legislature to restore $14 million in need-based scholarships. The Kentucky Student Voice Team is now an independent research and advocacy organization. Dedicated to centering the experience of young people in public policy, the organization recently announced the creation of a new youth-led education media service, The New Edu

Or Melati and Isabel Wijsen, sisters from Indonesia, who at the ages of 12 and 10 years old founded what is now one of the largest environmental nonprofits in their home province of Bali. In just one day, volunteers with their organization removed 65 tons of plastic waste from the environment and their reach extends beyond their home island–influencing policy and advocacy around the world.

In Pennsylvania, then-16-year-old Neil Deshmukh created PlantumAI, an app that uses artificial intelligence to help farmers address everything from crop disease and overuse of pesticides to unpredictable harvest schedules driven by climate change.

In Colorado, Gitanjali Rao has developed breakthrough technologies in early diagnosis of prescription opioid addiction and lead contamination in water, and created a cyberbullying prevention tool using AI and natural language processing. She's done all of this while in high school.

And in Iowa, then 17-year-old Dasia Taylor developed color-changing sutures to detect post surgical infections. The lower cost medical device uses beet juice to warn patients recovering from medical procedures of potential infection, making it well-suited for use in lower- and middle-income countries.

There are millions more young people like these–catalysts in their communities whose work will change the world in the years and decades ahead.

Rather than write off the value of a generation that includes today's sixth graders, Haidt and those like him should spend some time with Gen Z and see what is plainly in front of them: in a complex and changing world, it is so often our young people who are rising to the most pressing challenges we face.

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https://www.afterbabel.com/p/the-teen-mental-illness-epidemic

The Teen Mental Illness Epidemic Began Around 2012

This is not just "Kids these days"

Jon Haidt

From the first time I wrote about Gen Z in 2015 (with Greg Lukianoff, in our Coddling essay) through my most recent discussion in a December interview with Tunku Varadarajan in the Wall Street Journal, the main criticism I have heard is that I'm just another old man (I'm 59) shaking his fist and complaining about "kids these days," when in fact "the kids are alright." If that's true, then the first half of the Babel project—on what social media did to childhood and to teen mental health—is fatally flawed. Is the criticism valid?

1. The Case Against Me

Two responses to that WSJ essay do us the favor of collecting quotations from previous generations complaining about the behavior of youth. First, see this Twitter thread from Paul Fairie, titled A Brief History of Kids Today Are Spoiled

Fairie includes this 1925 gem: 

Remove the girl or boy of today from radio, the telephone, furnace heat, the automobile, the libraries, movies, and other forms of amusement and comfort––give them merely a jackknife and nature's unchanging wonders for amusement, and how would they fare? … Ennui would claim them for its own and … they would fare ill until returned to their accustomed habitat of convenience and plenty.

Second, see this essay by Mike Males in LA Progressive, titled Enough Youth-Bashing, which includes this: 

From Greek poet Hesiod to modern youth bashers led by psychology professors Jonathan Haidt and Jean Twenge and former first lady Michelle Obama, no one says anything new. Hesiod cornered the market with his "no hope for the future of our people" rant against the "reckless… frivolous youth of today" (700 BC).

And this:

Eon after eon, it's the same float going by. Socrates thought books made the young mentally weak. Panics over coffee, witches, jazz, dime novels, comics, TV, backwards-masked lyrics, Ozzy, Eminem, Tupac, Grand Theft Auto, Harry Potter's Hermione, Miley, cellphones, Facebook, sexting, social media… the endless ephebiphobic idiocies should be retitled, "I'm Superior!" and given their own dismal library shelf.

These critics make two valid points: First, you can find these criticisms in all recent generations and in some going back thousands of years.1 Second, the criticisms are often part of a larger moral panic that arises in response to any new consumer product––and especially any new technology––that "kids these days" are using. Social media clearly fits this pattern. (Robby Soave explained the dynamics of tech panics well in his 2021 book Tech Panic.)

The critics also gain support from empirical studies by psychologists John Protzko and Jonathan Schooler, whose 2019 essay in Science Advances was titled Kids these days: Why the youth of today seem lacking. Protzko and Schooler summarize their many studies showing that older people suffer from a variety of cognitive biases, such as that we each have biased and self-serving memories of what we were like at that age, and so we older people always find current younger people inferior and declining. 

In sum, it's reasonable to start with skepticism of my claim (with Jean Twenge) that there is an epidemic of mental illness that began around 2012, and that is related in large part to the transition to phone-based childhoods, with a special emphasis on social media.  It makes sense to embrace as a null hypothesis the skeptics' view that there is nothing to see here, just another moral panic, and the kids are fine. I am in full agreement that the burden of proof falls on me. 

But if you take that as your null hypothesis, then you should be open to evidence that the null hypothesis is false and this time is different. Anecdotes about kids who began cutting themselves the week after going on Instagram won't do. You'll want to see peer-reviewed studies and high-quality surveys showing 1) that there is in fact an epidemic of mental illness and 2) that phones and social media are substantial contributing causes. I am currently writing a book that makes both of these arguments: Kids In Space: Why Teen Mental Health is Collapsing. 

In the rest of this Substack post, I offer a preview of the evidence that a mental illness epidemic emerged around 2012. I won't directly address the issue of causality here. I'll do that in many future posts, and in the book. (You can find a short version of the argument in my 2022 Senate testimony.)  This post simply responds to the "kids these days" critics. I make the case that this time really is different. The kids have not been alright since the early 2010s. 

2. The Collaborative Review Doc

I have been collaborating with Jean Twenge (author of iGen, and the forthcoming Generations) and Zach Rausch (my research assistant) on a pair of collaborative reviews that are open-source Google documents where we collect all the evidence we can find, on both sides of each question, and we invite critics to add comments and studies. In this post, I present our document titled: Adolescent mood disorders since 2010: A collaborative review. In future posts on this Substack, I'll present many more collaborative review docs and explain why open-source Google docs are an essential adjunct to social science research, especially regarding social trends that are changing too fast for the slow gears of academic life to keep up with––phenomena like social media and its effects on teen mental health and on liberal democracy.

Here is the first half of the Table of Contents, to give you a sense of the layout. Please look around the doc itself. If you are a researcher or mental health expert, ask for commenting rights to add your own studies and criticisms. I am a devotee of John Stuart Mill who wrote that "he who knows only his own side of the case, knows little of that." Help me get this right.

Figure 1. The first part of the Table of Contents from Adolescent mood disorders since 2010: A collaborative review.

First, let's look at what Gen Z says about its own mental health, compared to previous generations. After that, we'll look at hard evidence about behavior, to address the criticism that the only thing that has changed is Gen Z's willingness to report their mental health problems.

3. Increases in Self-Reported Depression and Anxiety

Section 1 of the Collaborative Review summarizes self-report surveys that have been conducted at regular time intervals since 2010 or earlier. Do members of Gen Z say that their mental health is declining? Yes, in every study we can find. We cannot find any studies on the other side. I will focus today's post on data from the United States. I'll have a future post on what's happening internationally, showing that the same patterns are happening in largely the same way at roughly the same time in Canada, the UK, Australia, and New Zealand, and I'll share with you what Zach and I are learning about other countries beyond the Anglosphere. 

Here are two of the graphs you'll find in section 1 of the Collaborative Review document:

Figure 2. NSDUH data, graphed in 1.1.2 Twenge, Cooper, Joiner, Duffy, & Binau (2019), and re-graphed with more recent data by Haidt. Currently on p. 12 of the Collaborative Review doc.

As you can see in Figure 2, and in most of the Figures in the review doc, there was no sign of a problem before 2010, and the epidemic is well underway by 2015. You can also see that the rate of depression is much higher in girls, as is the absolute increase (since 2010 an additional 18% of girls suffered from depression in 2021, compared to an additional 6% of boys), however, the relative increase is similar in both sexes: around 150%. The rate had more than doubled before the covid epidemic. The 2020 data were collected in early 2020, just before covid restrictions, and the 2021 data were collected a year later before vaccines were widely available.* You can see that covid accelerated the rise in depression in that last year, but it was already rising really fast.

[*Correction, from Jean Twenge, added 2/15/23: It's the Monitoring the Future 2020 data that was all collected before the pandemic hit, not the NSDUH data, which was collected in both Q1 and Q4 of 2020. Either way, covid seems to have only added a bit of acceleration to a rapidly rising trend line]

Figure 3. American College Health Association (2019), National College Health Assessment. Study 1.1.17, currently on p. 36 of the Collaborative Review doc.

Figure 3 comes from a very different source: the mental health clinics on hundreds of college campuses. You can see once again that there's not much to see before 2010, but the epidemic is in full gear by 2015. You can also see that while rates of all disorders have increased, the increases are largest, in both relative and absolute terms, for mood disorders, a class of mental illness that is made up primarily of depression and anxiety disorders (which includes anorexia). In 2019, just before covid, one in four American college students suffered from an anxiety disorder, compared to just one in ten back in 2010. The rate may be higher today.

Section 1 of the collaborative review shows that according to the kids themselves, the kids are not alright. What happens when we ignore what they say and look at what they do?

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4. Increases in Self-Harm

In 2019, when Twenge and I launched the document, there were still some skeptics who argued that the large increases you see in Figures 1 and 2 reflect only a change in Gen Z's willingness to disclose their struggles, which is a good thing. Here is psychiatrist Richard Friedman, in the New York Times in 2018: 

"Despite news reports to the contrary, there is little evidence of an epidemic of anxiety disorders in teenagers… There are a few surveys reporting increased anxiety in adolescents, but these are based on self-reported measures — from kids or their parents — which tend to overestimate the rates of disorders because they detect mild symptoms, not clinically significant syndromes."

This argument is heard less often nowadays, but it was made by one critic recently in response to my WSJ interview. Here is Vicki Phillips, in Forbes, in an essay titled Gen Z: Hopeless Or Hopeful?

[Haidt] fails to note the remarkable truth. Gen Z is in fact embracing a new, more open and honest relationship with their mental health, one that destigmatizes the issue so that it can be addressed. This is leading to more people reporting their mental health challenges and seeking support, and contributing to the rising numbers of reported cases. More effective diagnoses and increased connection to care are both good by any measure. As an educator, I feel an obligation to support Haidt on his personal learning journey. And as someone who has actually worked with Gen-Zers, I can tell you, the kids these days are more than alright.

But if Phillips and Friedman were correct that "the kids are alright" and the appearance of an epidemic is an illusion based on Gen Z's "more honest relationship with their mental health," then we would not see any change in objective measures of mental health, such as hospitalizations for self-harm, or deaths by suicide. But in fact, we do see such changes, and the timing and magnitude of them generally match the changes in self-reported mental health problems. Sections 2 and 3 of the Collaborative Review doc report these findings.

Figure 4. Hospital admissions for self-harm, older teens (ages 15-19), CDC data. See section 2.1.1 of the collaborative review doc. 

Figure 4 shows the number per 100,000 older teens who are admitted each year to hospitals because they harmed themselves, mostly by cutting themselves with sharp objects. Once again there is no sign of a problem before 2010, and the epidemic is raging by 2015. 

Figure 5. Hospital admissions for self-harm, younger teens (ages 10-14), CDC data. See section 2.1.1 of the collaborative review doc. 

Figure 5 is the same as Figure 4 except that it shows what happened to younger teens, ages 10-14. Younger teens were very rarely hospitalized for self-harm before 2010, but by 2020 the rate for girls had nearly tripled, rising to exceed the rate at which older teen girls were hospitalized back in 2009. This is a clue as to what caused the epidemic. What could have changed right around 2012 that hit tween and young teen girls hardest? (I'll answer that in a later post.) 

5. Increases in Suicide

Section 3 of the Collaborative Review doc presents the most tragic data of all: a large increase in the number of completed suicides.

Figure 6. Suicide rate per 100,000 of US population, ages 15-19. Source: CDC, See section 3.1 of the Collaborative Review.

For suicide, the rates are always higher for boys and men. Girls and women make more suicide attempts, but they are more likely to use reversible means. Boys and men are more likely to use firearms and tall buildings, which are not reversible. Suicide takes a much larger toll on boys and men. But it is noteworthy that the relative increase since 2010 is larger for girls and women.2 

Figure 7. Suicide rate per 100,000 of US population, ages 10-14. Source: CDC, See section 3.1 of the Collaborative Review.

When we look at the change in suicide rates for tweens and younger teens, in Figure 7, we find three features that echo what we saw in the graphs for self-harm: 1) the percent increase is larger for girls than for boys, 2) the percent increase for young girls is much larger than the increase for older girls, and 3) even more than in Figure 4, there is a sharp increase for girls between 2012 and 2013. In fact, there was a 67% increase in suicides in that single year. This sudden and enormous spike, in a single year, once again forces us to ask: what changed in the lives of 10-14 year old girls in 2012? 

6. Conclusion

I began this essay by taking the burden of proof upon myself. Given the long history of tech panics, you should come to this question and this blog with skepticism. Your default assumption should be the null hypothesis so often asserted by my critics: this is just one more unjustified freakout by older people about "kids these days." 

But as I have shown in this post, the evidence that this time is different is very strong. In 2010 there was little sign of any problem, in any of the long-running nationally representative datasets (with the possible exception of suicide for young teen boys). By 2015––when Greg Lukianoff and I wrote our essay The Coddling of the American Mind––teen mental health was a 5 alarm fire, according to all the datasets that Jean Twenge and I can find. The kids are not alright. 

Please join me on the After Babel Substack to figure out why. In future posts I'll cover topics including these:

• What is happening to teen mental health in other countries? Is this just an American thing?

• What is the evidence that the epidemic is caused in large part by social media?

• What is the evidence that the loss of free play and risky play contributed to the epidemic? 

• What is happening to boys? They're not on social media as much, so why is their mental health deteriorating too?

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Nuances and complexities

• I started this Substack to help me write two books, as explained in the About post. In many posts, such as this one, I'll make a strong assertion in the title of the post in the hope of drawing criticism. Did the epidemic really start in 2012, or was it earlier? Was it gradual, not a sharp bend in 2012?  I want to hear all of the counterarguments and find all of the contrary evidence now, rather than waiting until after the book is published. So please be (constructively) critical in your comments. It is so hard to find time to write the books that I won't be able to respond to comments one by one, but I will read them all and I'll pull out the best criticisms and respond to them in new text at the bottom of this post. 

• It is beyond the scope of this short post to go into variations by race, SES, ideology, and other factors, but I have done so in Appendices A through G of the collaborative review doc. TLDR: there are some variations, such as that the increases in depression and anxiety were larger among sexual minorities, and among girls who self-identified as liberal. But the general trends are similar across all groups.

• The suicide rate for boys was higher in the 1980s than it is now. I believe those earlier levels, which used to rise and fall with the violent crime rate, were caused in part by the high and rising prevalence of lead in children's bloodstreams from the 1950s until leaded gas was banned in the late 1970s and early 1980s. Rates of suicide and violence then plummeted 15 years later, and sociologists have not yet converged upon an explanation. You can see graphs of these changes, and find arguments on both sides, in my debate with Chris Ferguson, in section 3.2.1 of the Collaborative Review doc.

Responses prompted by comments

See responses in post #2: The new CDC report shows that Covid added little to teen mental health trends.

1

Some of the quotations attributed to Ancient Greek sources may be inaccurate, but once Western societies began the modernization process in the 17th century and cross-generational change sped up, the quotations seem to increase in frequency. (You can find many more here).

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https://www.afterbabel.com/p/social-media-mental-illness-epidemic

Social Media is a Major Cause of the Mental Illness Epidemic in Teen Girls. Here's the Evidence.

Journalists should stop saying that the evidence is just correlational

Jon Haidt

A big story last week was the partial release of the CDC's bi-annual Youth Risk Behavior Survey, which showed that most teen girls (57%) now say that they experience persistent sadness or hopelessness (up from 36% in 2011), and 30% of teen girls now say that they have seriously considered suicide (up from 19% in 2011). Boys are doing badly too, but their rates of depression and anxiety are not as high, and their increases since 2011 are smaller. As I showed in my Feb. 16 Substack post, the big surprise in the CDC data is that COVID didn't have much effect on the overall trends, which just kept marching on as they have since around 2012. Teens were already socially distanced by 2019, which might explain why COVID restrictions added little to their rates of mental illness, on average. (Of course, many individuals suffered greatly). 

Most of the news coverage last week noted that the trends pre-dated covid, and many of them mentioned social media as a potential cause. A few of them then did the standard thing that journalists have been doing for years, saying essentially "gosh, we just don't know if it's social media, because the evidence is all correlational and the correlations are really small." For example, Derek Thompson, one of my favorite data-oriented journalists, wrote a widely read essay in The Atlantic on the multiplicity of possible causes. In a section titled Why is it so hard to prove that social media and smartphones are destroying teen mental health? he noted that "the academic literature on social media's harms is complicated" and he then quoted one of the main academics studying the issue—Jeff Hancock, of Stanford University: "There's been absolutely hundreds of [social-media and mental-health] studies, almost all showing pretty small effects."

In this post, I will show that Thompson's skepticism was justified in 2019 but is not justified in 2023. A lot of new work has been published since 2019, and there has been a recent and surprising convergence among the leading opponents in the debate (including Hancock and me). There is now a great deal of evidence that social media is a substantial cause, not just a tiny correlate, of depression and anxiety, and therefore of behaviors related to depression and anxiety, including self-harm and suicide.

First, I must offer two stage-setting comments:

1. Social media is not the only cause; my larger story is about the rewiring of childhood that began in the 1990s and accelerated in the early 2010s. 

I'm a social psychologist who is always wary of one-factor explanations for complex social phenomena. In The Coddling of the American Mind, Greg Lukianoff and I showed that there were six interwoven threads that produced the explosion of unwisdom that hit American universities in 2015, one of which was the rise of anxiety and depression in Gen Z (those born in and after 1996); a second was the vast overprotection of children that began in the 1990s. 

In the book I'm now writing (The Anxious Generation) I show that these two threads are both essential for understanding why teen mental health collapsed in the 2010s. In brief, it's the transition from a play-based childhood involving a lot of risky unsupervised play, which is essential for overcoming fear and fragility, to a phone-based childhood which blocks normal human development by taking time away from sleep, play, and in-person socializing, as well as causing addiction and drowning kids in social comparisons they can't win. So this is not a one-factor story, and in future posts I'll show my research on play. But today's post is about what I believe to be the largest single factor and the only one that can explain why the epidemic started so suddenly, around 2012, in multiple countries.

2. The empirical debate has focused on the size of the dose-response effect for individuals, yet much and perhaps most of the action is in the emergent network effects.

Once you appreciate the extent to which childhood has been transformed by smartphones and social media, you can see why it's a mistake to focus so narrowly on individual-level effects. Nearly all of the research––the "hundreds of studies" that Hancock referred to––have treated social media as if it were like sugar consumption. The basic question has been: how sick do individuals get as a function of how much sugar they consume? What does the curve look like when you graph illness on the Y axis as a function of daily dosage on the X axis? This is a common and proper approach in medical research, where effects are primarily studied at the individual level and our objective is to know the size of the "dose-response relationship."  (Although even in medicine, there are important network effects.)

But social media is very different because it transforms social life for everyone, even for those who don't use social media, whereas sugar consumption just harms the consumer. To see why this difference matters, imagine that in 2011, just before the epidemic began, a 12-year-old girl was given an iPhone 4 (the first with a front-facing camera) and began to spend 5 hours a day taking and editing selfies, posting them on Instagram (which had launched the year before), and scrolling through hundreds of posts from others. This was at a time when none of her friends in 7th grade had a smartphone or any social media accounts. Suppose that Instagram does cause anxiety disorders in a dose-response way, but the size of the correlation with anxiety is smaller than the correlation of social isolation with anxiety. The girl spending 5 hours a day on Instagram finds her mental health declining, but her friends' mental health is unchanged. We find a clear dose-response effect. If she were to quit Instagram, would her mental health improve? Yes.

But now fast forward to 2015, when most girls are on Instagram and all teens are spending far less time with their friends in person (as I showed in my Feb 16 post). Most social activity is now asynchronous—channeled through posts, comments, and emojis on Instagram, Snapchat, and a few other platforms. Childhood has been rewired—it has become phone-based—and rates of anxiety and depression are soaring (as I showed in my Feb 8 post). Suppose that in 2015, a 12-year-old girl decided to quit all social media platforms. Would her mental health improve? Not necessarily.

If all of her friends continued to spend 5 hours a day on the various platforms then she'd find it difficult to stay in touch with them. She'd be out of the loop and socially isolated. If the isolation effect is larger than the dose-response effect, then her mental health might even get worse. When we look across thousands of girls, we might find no strong or clear correlation between time on social media and level of mental disorder. We might even find that the non-users are more depressed and anxious than the moderate users (which some studies do find, known as the Goldilocks effect). 

What we see in this second case is that social media creates a cohort effect: something that happened to a whole cohort of young people, including those who don't use social media. It also creates a trap—a collective action problem—for girls and for parents. Each girl might be worse off quitting Instagram even though all girls would be better off if everyone quit.

An implication of this analysis is that the correlations we are about to look at probably underestimate the true effect of social media as a cause of the teen mental illness epidemic. But OK, let's take a look.

1. The State of the Art in 2019

In The Coddling of the American Mind, Greg Lukianoff and I tried to explain what happened to Gen Z. We focused on overprotection ("coddling"), but in our chapter on anxiety, we included six pages discussing the possible role of social media, drawing heavily on Jean Twenge's work in her book iGen. The evidence back in 2017, when we were writing, was mixed, so we were appropriately careful, ending the section with this:

We don't want to create a moral panic and frighten parents into banning all devices until their kids turn twenty-one. These are complicated issues, and much more research is needed.

Our book came out in September 2018. Four months later, two researchers at Oxford University—Amy Orben and Andrew Przybylski—published a study that was widely hailed as the most authoritative study on the matter. It was titled The association between adolescent well-being and digital technology use. The study used an advanced statistical technique called "Specification Curve Analysis" on three very large data sets in which teens in the US and UK reported their "digital media use" and answered questions related to mental health. Orben and Przybylski reported that the average regression coefficient (using screen time use to predict positive mental health) was negative but tiny, indicating a level of harmfulness so close to zero that it was roughly the same size as they found (in the same datasets) for the association of mental health with "eating potatoes" or "wearing eyeglasses." The relationships were equivalent to correlation coefficients less than r = .05 (where r = 1.0 indicates a perfect correlation and r = 0 indicates absolutely no relationship). The authors concluded that "these effects are too small to warrant policy change."

It is impossible to overstate the influence of Orben & Przybylski (2019) on journalists and researchers. The comparison to potatoes was vivid and memorable. Here's one writeup of the study:

Whenever you hear a journalist or researcher say that social media has been found to have little or no relationship with mental illness, you're likely to find a link to that study. When I first read the study, I began to have doubts myself. After all, it was the largest and most impressive study ever done on the question, and it was published by researchers who had been studying social media far longer than I had. Might Greg and I have gotten it wrong? Might we have been contributing to yet one more unjustified moral panic over technology? 

2. The Social Media and Mental Health Collaborative Review Doc

Many other studies came out in 2019, yielding conclusions on both sides of the question. It was a confusing time. So I decided to compile in one Google doc all the relevant studies I could find. I invited Jean Twenge to join me on the project since she was far more knowledgeable about the various datasets. We posted the Google doc online in February 2019 and invited comments from critics and the broader research community. Each section ends with a request to tell us what we have missed. One of the first comments we got was that some researchers doubted that the mental illness epidemic was real. That led us to create a second Google doc titled: Adolescent mood disorders since 2010: A collaborative review. (I described it in my Feb. 8 Substack post.) 

We immediately found that there was a simple and obvious structure for the social media literature review: nearly all of the published studies fell into one of three categories: correlational, longitudinal, or experimental. We therefore structured the document around the three questions addressed by studies of those types. You can see the three questions in the Table of Contents. I've reproduced the first part of it in Figure 1. Please check out the doc itself, and especially our list of "cautions and caveats." 

Figure 1. The first part of the Table of Contents of the Social Media and Mental Health Collaborative Review.

In the next four sections of this post, I'll briefly summarize what we've found about causality from those three kinds of studies (with the experiments section divided into two subtypes). 

3. Question 1: Is There an Association Between Social Media Use and Bad Mental Health Outcomes?

3.1 Overview

The typical study here asked hundreds or thousands of adolescents to report how much time they spend on social media, or digital media more generally, and then report something about their mental health. Of course, correlational studies can't prove causation, but they are a first step; they tell us what goes with what, and then we can figure out which way the causal arrows go later.  

The great majority of studies find a positive correlation between time on social media and mental health problems, especially mood disorders (depression and anxiety). At present, there are 55 studies listed in our review that found a significant correlation, and 11 that found no relationship, or nearly no relationship. The "winning side" is not determined by a simple count, as we explain in the "cautions and caveats" section. But the correlations are widely found and they are not randomly distributed. In fact, there is a revealing pattern found across many studies and literature reviews: Those that look at all screen-based activities (including television) for all kids (including boys) generally find only small correlations (usually less than r = .10), but as you zoom in on social media for girls the correlations rise, sometimes to r = .20, which is quite substantial, as I'll show in a moment.

The general finding in these correlational studies is a dose-response relationship such as the one in Figure 2, from Kelly, Zilanawala, Booker, & Sacker (2019) [Study 1.1.5 in the Review doc] who analyzed data from the large Millennium Cohort Study in the UK, which followed roughly 19,000 British children born around the year 2000 as they matured through adolescence.

Figure 2. Percent of UK adolescents with "clinically relevant depressive symptoms" by hours per weekday of social media use, including controls. Haidt and Twenge created this graph from the data given in Table 2 of Kelly, Zilanawala, Booker, & Sacker (2019), page 6.

Note three features of figure 2 that are common across many studies:

1. The rates of mood disorders are higher for girls than boys.

2. The lines are curved: moderate users are often no worse off than non-users, but as we move into heavy use, the lines rise more quickly. 

3. The dose-response effect is larger for girls. For boys, moving from 0 to 5 hours of daily use is associated with a doubling of depression rates. For girls, it's associated with a tripling. 

3.2 How Can We Square This Large Dose-Response Effect With the "Potatoes" Finding?

How can this large effect of social media use on girls be reconciled with the Orben and Przybylski study, which also examined the same UK dataset? 

Twenge and I argued in a published response paper in the same journal that Orben and Przybylski made 6 analytical choices, each one defensible, that collectively ended up reducing the statistical relationship and obscuring a more substantial association. The first issue to note is that the "potatoes" comparison was what they reported for all "digital media use," not for social media use specifically. Digital media includes all screen-based activities, including watching TV or Netflix videos, which routinely turn out (in correlational studies) to be less harmful than social media. In their own published report, when you zoom in on "social media," the relationship is between 2 and 6 times larger than for "digital media." Also crucial is that Orben and Przybylski combined all teens (boys and girls), while many studies have found that the correlations with mood disorders are larger for girls. So even if the association is weak for all kids using all screens, the association is much larger if you zoom in on girls using social media. (You can read Orben and Przybylski's response to our response here.)

Twenge and I later re-ran Orben and Przybylski's SCA on the same datasets (teaming up with researchers Kevin Cummins and Jimmy Lozano.) When we used Orben and Przybylski's assumptions, we replicated their results exactly, obtaining associations that were equivalent to correlation coefficients less than r = .05. But when we limited the analysis to social media for girls, we found relationships that were many times larger, equivalent to correlation coefficients of roughly r = .20.1 

3.3 A Surprising Convergence on the Size of the Association

Despite several years of heated debate, a consensus has emerged about just how large the correlation is between social media use and mood disorders. Amy Orben herself conducted a "narrative review" of many other reviews of the academic literature (Orben, 2020; study 5.7 in our review doc). Her conclusion is that "The associations between social media use and well-being therefore range from about r = − 0.15 to r = − 0.10."2 Similarly, Jeff Hancock and his team posted a meta-analysis in 2022, with data that went up through 2018 (Hancock, Liu, Luo & Mieczkowski, 2022, Study 5.27). They report very low associations (near zero) of social media use with some mental health outcomes, but when you zoom in on depression and anxiety as the outcome variables, they too report associations between r = .10 and r = .15. 

Both studies (Orben's and Hancock's) merged boys and girls together, and there is a consensus that the relationships are tighter for girls; see Kelly, Zilanawala, Booker, & Sacker (2019), Nesi & Prinstein (2015), and Twenge (2020). So we're reaching a consensus that for girls the true value is north of r = .15, which is consistent with the values around .20 that Twenge, Lozano, Cummins and I found in our SCA paper. This range of values, from r = .15 to r = .20, is quite large when we're talking about health effects found in large datasets, as I explain in this geeky footnote.3 It is much larger than the correlation between childhood exposure to lead and adult IQ (which was found to be around r = .11). And it is certainly much larger than the correlation between mood disorders and eating potatoes or wearing glasses. 

In fact, in our SCA paper, we compared the association of social media time with mental illness to other variables found in the same datasets. In that same UK dataset, mood disorders were more closely associated with social media usage than with marijuana use and binge drinking, though less closely associated with sleep deprivation. I'm not saying that a day of social media use is worse for girls than a day of binge drinking. I'm just saying that if we're going to play the game of looking through lists of correlations, the proper comparison is not potatoes and eyeglasses, it is marijuana use and binge drinking. 

To conclude this section: Few parents would knowingly let their daughters become heavy users of anything that was correlated r = .20 with mental illness (again, see Figure 2). The effects might be even larger for younger teen girls, who are just beginning puberty, according to a recent study by Orben, Przybylski, Blakemore, & Kievit (2022). Granted, these correlations don't prove causation, but the frequent finding that the correlations are consistently higher for social media, and higher for girls, tells us that we're not just looking at random noise here. There is a consistent story emerging from these hundreds of correlational studies. 

What would it take to show that social media use was causing teen girls to become depressed and anxious? Social scientists generally move on from correlational studies to longitudinal studies and true experiments.

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4. Question 2: Does Social Media Use at Time 1 Predict Anything About Mental Health at Time 2?

The second group of studies is known as longitudinal studies, in which hundreds or thousands of people are tracked over some time period and measured repeatedly. Typically, participants fill out the same survey once per year, allowing researchers to measure change over time in the same research participants. But these studies have an interesting property that allows researchers to infer causality; you can look to see if an increase or decrease in some behavior at one point in time predicts a change in other variables at the next measurement time. For example, let's say a teen reports that she spends two hours a day on social media, on average, across a 10-week study. If in week 3 she suddenly reduces her time to zero, what do we expect to happen to her mood in week four? Will she be happier or sadder? If there is, on average, a change in happiness the week after people quit or reduce their social media time, then we can infer that the change in mood was caused by the change in behavior the prior week. 

So what do we find in these studies? As I write this post in Feb. 2023, we have 40 longitudinal studies in section 2 of the Collaborative Review doc. Twenty-five of them (62.5%) found evidence indicating causation, and 15 of them largely failed to find such evidence. Once again, you can't just count up the studies and let the majority side win; studies that fail to find an effect are sometimes harder to publish. But our collaborative review docs make it easy to acquaint yourself with the range of studies and see what differentiates the studies that found evidence of harmful effects from those that did not. As we read through the studies in sections 2.1 and 2.2, we noticed something: the studies that used a short time interval (a week or less between measurements) mostly failed to find an effect, which makes sense if social media is addictive, as much evidence suggests. Going cold turkey doesn't make you happy, it makes you anxious and dysphoric for a few weeks, so we should not expect to find benefits to mental health in the short-interval studies. 

Zach Rausch (the lead researcher for this Substack) created a 2x2 table to categorize all the studies in section 2 as either a short interval (a week or less) or long interval (a month or more), and as finding an effect versus no effect. He found that 7 studies used a week or less (5 of them were daily), and only 1 of the 7 found an effect. But 33 studies used a month or more (20 were annual) and of these, 24 found a significant effect. So a simple dose-response model in which social media is like poison (where cutting consumption on Monday makes you feel better on Tuesday) does not seem to be supported. But 73% of the studies that looked for causal effects a month or more in the future found them.

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5. Question 3A: Do Experiments Using Random Assignment Show a Causal Effect of Social Media Use on Mental Health?

Now we come to the gold standard in the social sciences for testing causality: the experiment. Initially, we included all experiments in this section, but in the last year, several "quasi-experiments" that took advantage of natural variation have been published. I'll cover those in the next section. In this one, we'll just look at true experiments, in which participants were randomly assigned to either a treatment condition or a control condition, and then some dependent variable related to mental health was measured. Most experiments are done on college students or young adults—it's hard to get parental consent to do experiments on minors—so we did not limit this section to studies on adolescents. There are few experiments out there (compared to correlational and longitudinal studies), so we included some that used adults if it was clear that many or most were relatively young. 

In Feb. 2023 we have 18 true experiments in Section 3, of which 12 (67%) found evidence of a causal effect (Section 3.1) and 6 failed to find evidence of a causal effect (section 3.2). Some of the studies randomly assigned college students or young adults to reduce their social media use for a while and then measured self-reported mental health outcomes, compared to the control group which was instructed to make no changes. For example, Hunt, Marx, Lipson & Young (2018) randomly assigned college students to greatly reduce the use of social media platforms (or not reduce) and then measured their depressive symptoms four weeks later. They found that "The limited use group showed significant reductions in loneliness and depression over three weeks compared to the control group."

Some of the studies exposed girls and young women to time on Instagram, or to experiences designed to mimic Instagram, and then looked at the psychological aftereffects. For example, Kleemans, Daalmans, Carbaat, & Anschütz (2018) randomly assigned teen girls to be exposed either to original selfies taken from Instagram or to selfies that were manipulated to be extra attractive. "Results showed that exposure to manipulated Instagram photos directly led to lower body image." Engeln, Loach, Imundo, & Zola (2020) randomly assigned female college students to use Facebook, use Instagram, or perform an emotionally neutral task (the control condition) on an iPad. The finding: "Those who used Instagram, but not Facebook, showed decreased body satisfaction, decreased positive affect, and increased negative affect."

Turning to the six experiments that failed to find significant effects, it is noteworthy that four of these six experiments involved asking participants to reduce or eliminate social media for one week or less. As we saw in the examination of longitudinal studies, going "cold turkey" brings immediate discomfort to addicts; the benefits only kick in after a few weeks when the brain has adapted to the loss of chronic stimulation. So if we remove all of the studies that used a week or less (including two studies in section 3.1 that found an effect), the final tally becomes ten that found evidence that social media is harmful (80%) and two that did not. 

In sum, there are now many true experiments using a variety of methods to test questions such as whether reducing or eliminating exposure to social media confers benefits (it does, when continued for at least a month), or if exposing girls and women to Instagram or Instagram-like experiences damages their mood or body image (it does). These experiments provide direct evidence that social media—particularly Instagram—is a cause, not just a correlate, of bad mental health, especially in teen girls and young women.

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6. Question 3B: Do Quasi-experiments Using the Arrival of Facebook or High-speed Internet Show a Causal Effect of Social Media Access on Mental Health?

The previous three questions all asked about individual-level effects: what happens to individuals who are exposed to more or less social media? But this fourth category of studies is different—and very important—for this reason: it is the only one that allows us to look at emergent network effects. These studies look at how whole communities changed when social media suddenly became much more available in that community. These studies are sometimes called "quasi-experiments" because the researchers take advantage of natural variation in the world as though it was random assignment. 

For example, Braghieri, Levy, & Makarin (2022) took advantage of the fact that Facebook was originally offered only to students at a small number of colleges. As the company expanded to new colleges, did mental health change in the following year or two at those institutions, compared to colleges where students did not yet have access to Facebook? Yes, it got worse. The authors say: 

We find that the roll-out of Facebook at a college increased symptoms of poor mental health, especially depression, and led to increased utilization of mental healthcare services. We also find that, according to the students' reports, the decline in mental health translated into worse academic performance. Additional evidence on mechanisms suggests the results are due to Facebook fostering unfavorable social comparisons.

We also found five studies that used a similar design applied to the rollout of high-speed internet. It's hard to have a phone-based childhood when data speeds are very low. So what happened in Spain as fiber optic cables were laid and high-speed internet came to different regions at different times? Same thing, except with clearer evidence of a gendered effect. Arenas-Arroyo, Fernandez-Kranz, & Nollenberger (2022; study 3.3.2) analyzed "the effect of access to high-speed Internet (HSI) on hospital discharge diagnoses of behavioral and mental health cases among adolescents." Their conclusion:

We find a positive and significant impact on girls but not on boys. Exploring the mechanism behind these effects, we show that HSI increases addictive Internet use and significantly decreases time spent sleeping, doing homework, and socializing with family and friends. Girls again power all these effects.

They found that the arrival of high-speed internet had a particularly damaging effect on the quality of father-daughter relationships.

Guo (working paper, 3.3.1) did a similar study in British Columbia, Canada, where it took a long time for high-speed internet to reach rural areas. She found similar gendered effects: 

Estimates suggest high-speed wireless internet significantly increased teen girls' mental health diagnoses — by 90% — relative to teen boys over the period when visual social media became dominant among teenagers. I find similar effects across all subgroups, indicating they are not driven by differences in confounding characteristics.

Guo's study also verified that the arrival of high-speed wireless in each town was accompanied by an increase in interest in social media. 

In sum, we found six quasi-experiments that looked at real-world outcomes in real-world settings when the arrival of Facebook or high-speed internet created large and sudden emergent network effects. All six found that when social life moves rapidly online, mental health declines, especially for girls. Not one study failed to find a harmful effect.

7. Conclusion: Social Media Is a Major Cause of Mental Illness in Girls, Not Just a Tiny Correlate

We are now 11 years into the largest epidemic of teen mental illness on record. As the CDC's recent report showed, most girls are suffering, and nearly a third have seriously considered suicide. Why is this happening, and why did it start so suddenly around 2012?4 

It's not because of the Global Financial Crisis. Why would that hit younger teen girls hardest? Why would teen mental illness rise throughout the 2010s as the American economy got better and better? Why did a measure of loneliness at school go up around the world only after 2012, as the global economy got better and better? (See Twenge et al. 2021). And why would the epidemic hit Canadian girls just as hard when Canada didn't have much of a crisis?

It's not because of the 9/11 attacks, wars in the middle east, or school shootings. As Emile Durkheim showed long ago, people in Western societies don't kill themselves because of wars or collective threats; they kill themselves when they feel isolated and alone. Also, why would American tragedies cause the epidemic to start at the same time among Canadian and British girls?

There is one giant, obvious, international, and gendered cause: Social media. Instagram was founded in 2010. The iPhone 4 was released then too—the first smartphone with a front-facing camera. In 2012 Facebook bought Instagram, and that's the year that its user base exploded. By 2015, it was becoming normal for 12-year-old girls to spend hours each day taking selfies, editing selfies, and posting them for friends, enemies, and strangers to comment on, while also spending hours each day scrolling through photos of other girls and fabulously wealthy female celebrities with (seemingly) vastly superior bodies and lives. The hours girls spent each day on Instagram were taken from sleep, exercise, and time with friends and family. What did we think would happen to them?

The Collaborative Review doc that Jean Twenge, Zach Rausch and I have put together collects more than a hundred correlational, longitudinal, and experimental studies, on both sides of the question. Taken as a whole, it shows strong and clear evidence of causation, not just correlation. There are surely other contributing causes, but the Collaborative Review doc points strongly to this conclusion: Social Media is a Major Cause of the Mental Illness Epidemic in Teen Girls.

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Addendum, April 19 2023: Here are the published critiques I have found from fellow researchers who disagree with my conclusions.

Don't panic about social media harming your child's mental health – the evidence is weak. By Stuart Ritchie

"Some" Are Misrepresenting CDC Report Findings Specific To The Use Of Social Media & Technology By Youth. By The White Hatter   

Why I'm Skeptical About the Link Between Social Media and Mental Health.  By Dylan Selterman

The Statistically Flawed Evidence That Social Media Is Causing the Teen Mental Health Crisis, by my NYU colleague Aaron Brown, at Reason.com.

Here is my response (published on April 17, 2023): Why Some Researchers Think I Am Wrong About Social Media and Mental Illness

Addendum August 9, 2023: David Stein's substack The Shores of Academia is offering a series of helpful critiques of my work. In Social Media and Time Displacement he critiques this post and makes the valid points that I do not provide evidence that tween girls are spending so much time on social media (5 hours a day is surely well above average, even if you include time not ON the app but thinking about it or taking photos, which I did). He also correctly notes that I have not defined terms, or concentrated enough on all the other screen-based activities that tweens and teens are doing. I am drawing on Stein's critiques to improve the story in The Anxious Generation.

Footnotes:

1

There was one other difference that turned out to make a large difference in our results. Orben and Przybylski had not only controlled for demographic variables (such as race and parents' educational levels, which is universally done); they also controlled for some psychological variables that are potential mediators of a relationship between social media usage and poor mental health, such as negative attitudes about school and closeness with parents. We found that controlling for these psychological variables heavily suppressed the relationship between social media use and poor mental health. 

2

The correlations are negative because she framed this as "well-being"; they are positive if we talk about mental illness.

3

Many researchers learned in graduate school that a correlation coefficient of r = .5 and above is a "large" correlation, r = .3 and above is a "medium" sized correlation, and r = .10 and above is a "small" correlation, with r < .10 being trivial, not even "small." But recently, psychologists have noted that these cutoffs make no sense; what counts as large or small varies by domain. The key paper here is Gotz, Gosling, and Rentfrow (2020). Small Effects: The Indispensable Foundation for a Cumulative Psychological Science. The authors note that in the domains of public health and education, many of the things that warrant public expenditure are correlated with outcomes in the ballpark of r = .05 to r = .15. For example, Gotz et al. note that the correlation of calcium intake and bone mass in pre-menopausal women is r = .08, which is enough to recommend that women take calcium supplements. The correlation between childhood lead exposure and adult IQ is r = .11, which is enough to justify a national campaign to remove lead from water supplies.  These correlations are smaller than the links between mood disorders and social media use for girls. Gotz et al. note that such putatively "small" effects can have a very large impact on public health when we are examining  "effects that accumulate over time and at scale", such as millions of teens spending 20 hours per week, every week for many years, trying to perfect their Instagram profiles while scrolling through the even-more-perfect profiles of other teens.

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