Data

The Sins of TikTok, Part 1: Extreme Privacy Theft by China-Based Company

Scott Buchanan1 Comment

Social media apps are nosy by nature; it is no secret that their main business model is to snoop out information about you, the user, and package and sell that information to advertisers who can target you. But there is one wildly popular app which goes beyond the norms of intrusiveness and privacy invasion AND is targeted largely at children and adolescents AND is based in China and thus is subject to Big Brother’s request for any and all data. That app is TikTok.

To avoid a bunch of re-wording, I will largely share excerpts from “ The Privacy Risks of TikTok – Why This Invasive App is So Dangerous “ by Priscilla Sherman at VPNOverview. Other articles echo her concerns with TikTok:

TikTok is an extremely popular social media video app owned by the Chinese tech company ByteDance. On TikTok, users can create and share short-form videos using a variety of filters and effects. The platform is full of dancing, comedy, and other entertaining videos….

Several agencies and news outlets are now sounding the alarm and reporting on the many problems that have surfaced. ByteDance claims to want to break away from its Chinese background in order to serve a global audience and says it will never share data with the Chinese government. This claim, however, seems impossible now that new security laws have been introduced in Hong Kong.

TikTok’s user base mostly consists of children and adolescents, which many consider to be vulnerable groups. This is a main reason for different authorities to express their worries. However, it isn’t just the youth that might be in danger from TikTok. From December 2019 onwards, U.S. military personnel were no longer allowed to use TikTok, as the app was considered a ‘cyber threat’…

[Hacker group] Anonymous has published a video listing the many dangers of TikTok. They quote a source that has done extensive research on TikTok: “Calling it an advertising platform is an understatement. TikTok is essentially malware that is targeting children. Don’t use TikTok. Don’t let your friends and family use it. Delete TikTok now […] If you know someone that is using it, explain to them that it is essentially malware operated by the Chinese government running a massive spying operation.”

These claims fit in with the recent developments surrounding TikTok. For example, Apple researchers announced that TikTok deliberately spies on users.

Claims keep piling up, showing that TikTok is a very invasive application that poses a substantial privacy risk. It seems that the data collection at TikTok goes much further than other social platforms such as Facebook or Instagram. This is surprising, since both of these companies have already faced backlash for the way they’ve dealt with user privacy. TikTok seems to collect data on a much larger scale than other social media platforms do. This, combined with TikTok’s origins makes it quite plausible that the Chinese government has insight into all of this collected data…..

Research from a German data protection website has revealed that TikTok installs browser trackers on your device. These track all your activities on the internet. According to ByteDance, these trackers were put in place to recognize and prevent “malicious browser behavior”. However, they also enable TikTok to use fingerprinting techniques, which give users a unique ID. This enables TikTok to link data to user profiles in a very targeted way.

Unfortunately, this happens with a great disregard of privacy – perhaps intentionally so. The German researchers indicate, for example, that IP addresses aren’t anonymized when TikTok uses Google Analytics, meaning your online behavior is directly linked to your IP address. An IP address provides information about your location and, indirectly, about your identity…

A user on Reddit used reverse engineering to figure out more about TikTok. Anonymous quoted the results in the video we mentioned earlier. The Reddit user discovered that TikTok collects all kinds of information:

  • Your smartphone’s hardware (CPU type, hardware IDs, screen size, dpi, memory usage, storage space, etc.);
  • Other apps installed on your device;
  • Network information (IP, local IP, your router’s MAC address, your device’s MAC address, the name of your Wi-Fi network);
  • Whether your device was rooted/jailbroken;
  • Location data, through an option that’s turned on automatically when you give a post a location tag (only happens on some versions of TikTok);

Additionally, the app creates a local proxy server on your device, which is officially used for “transcoding media”. However, this is done without any form of authentication, making it susceptible to misuse….

We asked investigative journalist and writer Maria Genova about her vision on TikTok. … Genova says: “There’s a reason several countries have banned it. It’s unbelievable how much information an app like that pulls from your phone”…

TikTok needs access to your camera and microphone in order to work properly… However, there aren’t any specifications explaining how exactly these permissions are used. Therefore, TikTok could theoretically record conversations and sounds using your microphone, even when you aren’t filming a TikTok video.

We could go on and on with the technical details here, but you get the point. The fact that “IP addresses aren’t anonymized“ is really a big, bad deal. The article concludes:

The current findings and concerns surrounding TikTok are reason enough for us [the staff at VPNOverview] to remove the app from our devices. Whether TikTok’s main target group – young people between 14 and 25 – is sensitive to the privacy concerns that have come to light, remains to be seen.

Indeed.

One more quote , from Brendan Carr of the U.S. Federal Communications Commission (FCC), regarding the reliability of TikTok’s claims that they do not share data with the Chinese government:

“China has a national security law that compels every entity within its jurisdiction to aid its espionage and what they view as their national security efforts,” Carr said earlier this year, alluding to the fact that Chinese companies must make all the data they collect available to the Chinese Communist Party (CCP).

Stay tuned for Part 2, dealing with some larger market ramifications of TikTok’s evasion of  Apple and Android privacy protections.

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

This just in from BuzzFeed (added to original post here):

“Leaked Audio From 80 Internal TikTok Meetings Shows That US User Data Has Been Repeatedly Accessed From China”

For years, TikTok has responded to data privacy concerns by promising that information gathered about users in the United States is stored in the United States, rather than China, where ByteDance, the video platform’s parent company, is located. But according to leaked audio from more than 80 internal TikTok meetings, China-based employees of ByteDance have repeatedly accessed nonpublic data about US TikTok users — exactly the type of behavior that inspired former president Donald Trump to threaten to ban the app in the United States.

The recordings, which were reviewed by BuzzFeed News, contain 14 statements from nine different TikTok employees indicating that engineers in China had access to US data between September 2021 and January 2022, at the very least. Despite a TikTok executive’s sworn testimony in an October 2021 Senate hearing that a “world-renowned, US-based security team” decides who gets access to this data, nine statements by eight different employees describe situations where US employees had to turn to their colleagues in China to determine how US user data was flowing. US staff did not have permission or knowledge of how to access the data on their own, according to the tapes.

“Everything is seen in China,” said a member of TikTok’s Trust and Safety department in a September 2021 meeting.

A Dragonfly’s View of Election Day 2022

James Bailey3 Comments

This is my last post before the US midterm elections on Tuesday, so I’ll leave you with a prediction for what’s coming.

Who is the best predictor of elections? Nate Silver at FiveThirtyEight has had a pretty good run since 2008 using weighted polls. Ray Fair, an economics professor at Yale has a venerable and well-credentialed model based on fundamentals. I typically favor prediction markets, because they incorporate a wide range of views weighted by how willing people are to put their money where their mouth is, and traders are able to incorporate other sources of information (including predictors like FiveThirtyEight). But which prediction market should we trust? There are now many large prediction markets, and the odds often differ substantially between them.

When there are many reasonable ways of answering a question or looking at a problem, it can be hard to choose which is best. Often the best answer is not to choose- instead, take all the reasonable answers and average them. Dan Gardner and Philip Tetlock call this approach Dragonfly Eye forecasting, since dragonfly’s eyes see through many lenses. So what does the dragonfly see here?

Lets start with the US House, since everyone covers it.

  • FiveThirtyEight’s latest forecast shows that Republicans have an 85% chance of taking the House; it shows a range of possible outcomes, but on average predicts that Republicans win the popular vote by 4.3% and take 231 House seats (substantially over the 218 needed for a majority)
  • The Fair Model predicts that Democrats will win 46.6% of the two-party vote share (leaving Republicans with 53.4%). This has Republicans winning the popular vote by 6.8%, a moderately bigger margin than FiveThirtyEight. The reasoning is interesting; the economy is roughly neutral since “the negative inflation effect almost exactly offsets the positive output effect”, so this is mainly from the typical negative effect of having an incumbent party in the White House.
  • Prediction markets: PredictIt currently gives Republicans a 90% chance to take the House. Polymarket gives them 87%. Insight Prediction also gives them 87%. Kalshi doesn’t have a standard market on this, but their contest (free to enter, 100k prize) predicts 232 Republican seats.

Its a bit tricky to average all these since they don’t all report on the same outcome in the same way. But the overall picture is clear: Republicans are likely to do well in the House, with an ~87% chance to win a majority, expected to win the popular vote by ~5.55% and take ~232 seats.

The Senate is closer to a coin flip and harder to evaluate.

  • FiveThirtyEight gives Republicans a 53% chance to win a majority (51+ seats for them; Democrats effectively win if the Senate stays 50-50 since a Democratic Vice President breaks ties for at least 2 more years). The most likely seat counts are 50-50 or 51-49, but confidence intervals are pretty wide and 54-46 either direction isn’t ruled out.
  • The Fair Model doesn’t make Senate predictions, only House and Presidential predictions.
  • Prediction markets: PredictIt gives Republicans a 70% chance to win a Senate majority, probably with 52-54 seats. PolyMarket gives Republicans a 65% chance, as does Insight Prediction. Kalshi predicts 53 Republican seats.

Overall we see a much higher variance of predictions in the Senate; a 17pp gap between the highest (70%) and lowest (53%) estimates of Republican chances, vs just a 5pp gap for the House (90% to 85%). This shows up with the seat counts too; everyone agrees there’s a substantial chance Republicans lose the Senate, but if they do win, it will probably be by more than one seat. The average estimate is ~52 Republican seats. FiveThirtyEight and PredictIt agree that the closest Senate races will be Georgia, Pennsylvania, Arizona, Nevada, and New Hampshire (though they rank order them differently), so those are the races to watch.

Forecasts for governors aren’t as comprehensive, but FiveThirtyEight predicts we’ll get about 28 Republican (22 Democratic) governors, while PredictIt expects 31+ Republicans; I’ll split the difference at 30. Everyone agrees that Oregon is surprisingly competitive because of an independent drawing Democratic votes. The biggest difference I see is on New York, where PredictIt gives Republican challenger Lee Zeldin a real chance (26%) but FiveThirtyEight doesn’t (3%).

Overall forecast: moderate red wave, Republicans take the House and most governorships, probably the Senate too. But if they lose anything it is almost certainly the Senate.

These forecasts seem about right to me. Democrats are weighed down by an unpopular (-11) President and the highest inflation in 40 years. This would lead to a huge red wave, but Republicans have their own weaknesses; an unpopular former President lurking in the background, and the Supreme Court making a big unpopular change voters blame them for. This shrinks the red wave, but I don’t think its enough to eliminate it. The effect of Roe repeal is fading with time, and the unpopular Biden is more salient than the unpopular Trump; Biden is the one in office and is more prominent in media coverage. Facebook and recently-acquired Twitter may be doing Republicans a favor by keeping Trump banned through Election day. But if he drags Republicans down anywhere, it will be the Senate, where candidate quality (not just party affiliation) is crucial and his endorsements pushed some weak/weird/extreme candidates through primaries. We’ll also see this “extremist” Trump effect (abetted by cynical Democratic donations to extreme-right candidates) dragging down Republicans in some key governor’s races like Pennsylvania, where Democrats are now 90/10 favorites..

What’s Killing Men Ages 18-39?

Jeremy Horpedahl4 Comments

The all-cause mortality rate in 2021 for men in the US ages 18-39 was about 40% higher than the average of 2018 and 2019. That’s a huge increase, especially for a group that is not in the high-risk category for COVID-19. What’s causing it?

Some have suggested that heart disease deaths, perhaps induced by the COVID vaccines, is the cause. This is not just a fringe internet theory by anonymous Twitter accounts. The Surgeon General of Florida has said this is true.

What do the data say? The first thing we can look at is heart disease deaths for men ages 18-39.

The data I’m using is from the CDC WONDER database. This database aggregates data from US states, using a standardized system of reporting deaths. The most important thing to know is that in this database, each death can one have one underlying cause, and this is indicated on the death certificate. Deaths can also have multiple contributing causes (and most deaths do), and the database allows you to search for those too. But for this analysis, I’m only looking at the underlying cause.

Here’s the heart disease death data for men ages 18-39, presented two different ways. First the trailing 12-month average. Don’t focus too much on that dip at the end, since the most recent data is incomplete. Instead, notice three things. First, there was a clear increase in heart disease deaths. Second, that rise began in mid-2020, well before the introduction of vaccines. Third, once vaccines started being administered to this age group in Spring 2021, the number of deaths leveled off (though it didn’t return to pre-pandemic levels).

Here’s another way of looking at the data: 12-month time periods, rather than a trailing average. I created 12-month time periods starting in March and ending in February of the following year. I’ve also truncated the y-axis to show more detail, not to trick you. But don’t be tricked! The deaths are up 2-3%, not a more than doubling as the chart appears to show.

We can see in the chart above that the rise in heart disease deaths for young males completely preceded the vaccination period. Something changed, for sure, but the change wasn’t the introduction of vaccines. Heart disease deaths (by underlying cause) are only up 2-3%, while overall deaths are up around 40%.

So, to repeat the title question, what is killing these young men?

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Cheers to Sumproduct!

Zachary Bartsch2 Comments

I teach macroeconomics, finance, and other things.

Often, I use Excel to complete repetitive calculations for my students. The version that I show them is different from the version that I use. They see a lot more mathematical steps displayed in different cells, usually with a label describing what it is. But when I create an answer calculator or work on my own, I usually try to be as concise as possible, squeezing what I can into a single cell or many fewer cells. That’s what brings me to to the sumproduct excel function that I recently learned. It’s super useful I’ll illustrate it with two examples.

Example 1) NGDP

One way to calculate NGDP is to sum all of the expenditures on the different products during a time period. The expenditures on a good is simply the price of the good times the quantity that was purchased during the time period. The below image illustrates an example with the values on the left, and the equations that I used on the right. That’s the student version. There is an equation for each good which calculates the total expenditure on the individual goods. Then, there is a final equation which sums the spending to get total expenditures, or NGDP.

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The Only Analysis of the Pennsylvania Senate Debate That You Need To Read

Jeremy Horpedahl1 Comment

Last night the major party candidates for Senate in Pennsylvania had their first and only debate. I didn’t watch it, since I don’t live in Pennsylvania. But judging by my Twitter feed, a lot of people did watch it, including (bizarrely to me) lots of people who don’t live in Pennsylvania. And overnight, tons of articles were written analyzing the debate, saying who “won” the debate, and so on (“5 Things You Need to Know About the Pennsylvania Senate Debate” etc.).

But this blog post is the only thing you need to read about that debate. And these charts are really all you need to look at.

These two charts come from the prediction market website PredictIt. The charts show the “odds” (more on that below) that each candidate will win the Pennsylvania Senate race, over a 90-day time horizon (first chart) and the last 24 hours (second chart). What do we see? The Democratic candidate has been leading for the entire race up until a week ago, though with his odds falling gradually over the past month or two.

Notice though the big jump last night during the debate. The Republican candidate moved up from odds of about 57% to odds of about 63%, close to where it stands as I write (67%). Based on this result, it’s safe to say that the Republican candidate “won” the debate, though not so decisively that the election is now a foregone conclusion. You don’t need to wait for the polls, which have consistently showed the Democratic candidate in the lead (though with the gap closing in recent weeks) — though of course, these betting odds could change as new polling data is released.

But where do these odds come from?

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What do they even want?: Inflation Edition

Zachary BartschLeave a comment

People were all excited last week when the CPI numbers were released because… the year-over-year rate of inflation did a whole lot of nothing. See below. The 12-month rate of inflation was practically constant. The 8.2% number was all over the headlines and twitter. We already know that news outlets don’t always report on the most relevant numbers. And I say that this is one of those times.

https://fred.stlouisfed.org/graph/?g=UQ4T

First of all, there is a problem with the year-over-year indicator. Well, not so much problem in the measure itself, but more a problem of interpretation. The problem is that the 12-month rate of inflation is the cumulative compound rate for 12 individual months. Each month that we update the 12-month inflation rate, we drop a month from the back of the 12-month window and we add a month to the front of the 12-month window. Below are both a graph and a table indicating the monthly rate of inflation and the 12-month periods ending in August 2022 (pink) and in September 2022 (green).

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Mortgage Affordability Since 1971

Jeremy Horpedahl2 Comments

Mortgage interest rates are climbing quickly, while housing prices are still mostly high. These factors combined means that it is much more expensive to buy a home than in the recent past. But how much more expensive? And how does this compare with the past 50 years of history?

The chart below is my attempt to answer those questions. It shows the number of hours you would need to work at the average wage to make a mortgage payment (principal and interest) on the median new home in the US.

My goal here was to provide the most up-to-date estimate of this number consistent with the historical data. Thus, I had to use average wage data rather than median wage data, since the median hourly wage data is not available for 2022 yet. But as I’ve discussed before, while median and average wages are different, their rate of increase is roughly the same year-to-year, so it would show the same trends.

The final point plotted on the blue line in the chart is for August 2022, the last month for which we have median home price data, average wage data, and 30-year mortgage rates. Mortgage rates are the yearly average (or monthly average in the case of August 2022).

You’ll also notice a red dot at the very end of the series. This is my guess of where the line will be in October 2022, once we have complete data for these three variables (right now only mortgage rates are available in October for the three series I am using). I’m doing my best here to provide as much of a real-time picture as possible, given that rates are rising very sharply right now, while still providing consistent historical comparisons. If that estimate is roughly correct, mortgage costs on new homes are now less affordable than any year since 1990.

What do you notice in the chart?

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Norway is a Wealthy Country (even after making the appropriate adjustments)

Jeremy HorpedahlLeave a comment

I have previously wrote about living standards in Ireland, and how GDP per capita overstates typical incomes because of a lot of foreign investment.

This is not to say that foreign investment is bad — to the contrary! But standard income statistics, such as GDP, aren’t particularly useful for a country like Ireland.

Norway has a similar challenge with national income statistics, but a different reason: Oil. Norway has a very large supply of oil revenues relative to the size of the rest of its economy, and oil revenues are counted in GDP. But those oil revenues don’t necessarily translate into higher household income or consumption.

Using World Bank data, Norway appears to be very rich: GDP per capita in nominal terms was about $90,000 in 2021. Compare that with $70,000 in the US, which is a very rich country itself. Sounds extremely wealthy!

Of course, by that same statistic, average income in Ireland is $100,000. But after making all the proper adjustments, as we saw in my prior post, Ireland is right around the EU average in terms of what individuals and households actually consume.

What if we make similar adjustments for Norway?

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Willingness to be Paid Paper Accepted

Joy Buchanan4 Comments

I am pleased to announce that my paper “Willingness to be Paid: Who Trains for Tech Jobs?” has been accepted at Labour Economics.

Having a larger high-skill workforce increases productivity, so it is useful to understand how workers self-select into high-paying technology (tech) jobs. This study examines how workers decide whether or not to pursue tech, through an experiment in which subjects are offered a short programming job. I will highlight some results on gender and preferences in this post.

Most of the subjects in the experiment are college students. They started by filling out a survey that took less than 15 minutes. They could indicate whether or not they would like an invitation for returning again to do computer programming.

Subjects indicate whether they would like an invitation to return to do a one-hour computer programming job for $15, $25, $35, …, or $85.[1]This is presented as 9 discrete options, such as:

“I would like an invitation to do the programming task if I will be paid $15, $25, $35, $45, $55, $65, $75 or $85.”,

or,

“I would like an invitation to do the programming task if I will be paid $85. If I draw a $15, $25, $35, $45, $55, $65 or $75 then I will not receive an invitation.”,

and the last choice is

“I would not like to receive an invitation for the programming task.”

Ex-ante, would you expect a gender gap in the results? In 2021, there was only 1 female employee working in a tech role at Google for every 3 male tech employees. Many technical or IT roles exhibit a gender gap.

To find a gender gap in this experiment would mean female subjects reject the programming follow-up job or at least they would have a different reservation wage. In economics, the reservation wage is the lowest wage an employee would accept to continue doing their job. I might have observed that women were willing to program but would reject the low wage levels. If that had occurred, then the implication would be that there are more men available to do the programming job for any given wage level.

However, the male and female participants behaved in very similar ways. There was no significant difference in reservation wages or in the choice to reject the follow-up invitation to program. The average reservation wage for the initial experiment was very close to $25 for both males and females. A small number of male subjects said they did not want to be invited back at even the highest wage level. In the initial experiment, 5% of males and 6% of females refused the programming job.

The experiment was run in 3 different ways, partly to test the robustness of this (lack of) gender effect. About 100 more subjects were recruited online through Prolific to observe a non-traditional subject pool. Details are in the paper.

Ex-ante, given the obvious gender gap in tech companies, there were several reasons to expect a gender gap in the experiment, even on a college campus. Ex-post, readers might decide that I left something out of the design that would have generated a gender gap. This experiment involves a short-term individual task. Maybe the team culture or the length of the commitment is what deters women from tech jobs. I hope that my experiment is a template that researchers can build on. Maybe even a small change in the format would cause us to observe a gender gap. If that can be established, then that would be a major contribution to an important puzzle.

For the decisions that involved financial incentives, I observed no significant gender gaps in the study. However, subjects answered other questions and there are gender gaps for some of the self-reported answers. It was much more likely that women would answer “Yes” to the question

If you were to take a job in a tech field, do you expect that you would face discrimination or harassment?

I observed that women said they were less confident if you just asked them if they are “confident”. However, when I did an incentivized belief elicitation about performance on a programming quiz, women appear quite similar to men.

Since wages are high for tech jobs, why aren’t more people pursing them? The answer to that question is complex. It does not all boil down to subjective preferences for technical tasks, however in my results enjoyment is one of the few variables that was significant.

People who say they enjoy programming are significantly more likely to do it at any given wage level, in this experiment.

Fig. 3 Histogram of reservation wage for programming job, by reported enjoyment of computer programming (CP) and gender, pooling all treatments and samples

Figure 3 from the paper shows the reservation wage of participates from all three waves. Subjects who say that they enjoy programming usually pick a reservation wage at or near the lowest possible level. This pattern is quite similar whether you are considering males or females.

Interestingly, enjoyment mattered more than some of the other factors that I though would predict willingness to participate. About half of subjects said they had taken a class that taught them some coding, but that factor did not predict their behavior in the experiment. Enjoyment or subjective preferences seemed to matter more than training. To my knowledge, policy makers talk a lot about training and very little about these subjective factors. I hope my experiment helps us understand what is happening when people self-select into tech. Later, I will write another blog about the treatment manipulation and results, and perhaps I will have the official link to the article by then.

Buchanan, Joy. “Willingness to be Paid: Who Trains for Tech Jobs.” Labour Economics.

[1] We use a quasi-BDM to obtain a view of the labor supply curve at many different wages. The data is not as granulated as that which a traditional Becker-DeGroot-Marschak (BDM) mechanism obtains, but it is easy for subjects to understand. The BDM, while being theoretically appropriate for this purpose, has come under suspicion for being difficult for inexperienced subjects to understand (Cason and Plott, 2014). We follow Bartling et al. (2015) and use a discrete version.

Millennials Have Caught Up to Boomers: Generational Wealth Update (2022q2)

Jeremy Horpedahl2 Comments

Last week I wrote about wealth growth during the pandemic, but my favorite way to look at wealth data is comparing different generations. Last September I wrote a post comparing Boomers, Gen Xers, and Millennials in wealth per capita at roughly the same age. At the time, Millennials were basically equal to Gen X at the same age, and we were a year short of having comparable data with Boomers.

What does it look like if we update the chart through the second quarter of this year?

I won’t explain all of the data in detail — for that see my post from last September. I’ll just note a few changes. We now have single-year population estimates for 2020 and 2021, so I’ve updated those to the most recent Census estimates for each cohort. Inflation adjustments are to June 2022, to match the end of the most recent quarter of data from the Fed DFA. We still have to use average wealth rather than median wealth for now, but the Fed SCF is currently in progress so at some point we’ll have 2022 median data (most recent currently is 2019, and there’s been a lot of wealth growth since then).

What do we notice in the chart? First, we now have one year of overlap between Boomers and Millennials. And it turns out… they are pretty much at the same level per capita! Millennials have also now fallen slightly behind Gen X at the same time, since they’ve had no wealth growth (in real, per capita terms) since the end of 2021 to the present.

But Millennials have fared much better in 2022 with the massive drop in wealth: about $6.6 trillion in total wealth in the US was lost (in nominal terms) from the first to the second quarter of 2022. None of that wealth loss was among Millennials, instead it was roughly evenly shared among the three older generations (Boomers hid hardest). This difference is largely because Millennials hold more assets in real estate (which went up) than in equities (which went way down). The other generations have much more exposure to the stock market at this point in their life.

You can clearly see that affect of the 2022 wealth decline if you look at the end of the line for Gen X. You can’t see the effect on Boomers, since I cut off the chart after the last Gen X comparable data, but they saw a big decline since 2021 as well: about 6% per capita, along with 7% for Gen X. Even so, Gen X is still about 18% wealthier on average than Boomers were at the same age.

Of course, even since the end of the second quarter of 2022, we’ve seen further declines in the stock market, with the S&P 500 down about 4%. And who knows what the next few months and quarters will bring. But as of right now, Millennials don’t seem to be doing much worse than their counterparts in other generations at the same age.