Macro Roundup

"...In 1986, workers’ median weekly earnings jumped nearly 16% when they moved into that age bracket from their 20s and early 30s, according to BLS data. In 2005, as the oldest members of Gen X crossed the same age threshold, workers’ median weekly earnings increased by 20%. In 2020, workers saw a 22% increase in income when transitioning from the 25-to-34 age bracket to 35 to 44, which means older millennials entering this age range this year are on track to notch similar increases in income...."

Julia Carpenter, "Millennials’ High-Earning Years Are Here, but It Doesn’t Feel That Way,"Wall Street Journal, August 12, 2021, https://www.wsj.com/articles/millennials-high-earning-years-are-here-but-it-doesnt-feel-that-way-11628769603

Millennials’ High-Earning Years Are Here, but It Doesn’t Feel That Way

The oldest millennials are approaching a new money milestone: their high-earning years.

After two recessions and a world-changing pandemic, the arrival of the high-earning years for millennials born in the 1980s are around the corner. Yet data suggest this phase of life might not provide the financial security other generations found at the same age.

With a higher debt-to-income ratio and other financial obligations already pressing on their budgets, some millennials entering their high-earning years have delayed homeownership and family formation, said Lowell R. Ricketts, a data scientist for the Institute for Economic Equity at the Federal Reserve Bank of St. Louis.

“In perspective, you can kind of understand how the millennial generation is a microcosm of the K-shaped recovery that we’re seeing and also just the divergence in wealth overall,” Mr. Ricketts said. “I think you have to factor in the labor market changes over time as well, but there’s a kind of more of a sense of insecurity, even though you might be now earning a high salary or wage, that might not be guaranteed tomorrow.”

Workers hit their peak median weekly earnings between the ages of 35 and 54, according to the Bureau of Labor Statistics. After that period, individual earnings typically decrease or plateau, but workers experience the greatest gain in earnings when they jump from the 25-to-34 to the 35-to-44 age bracket.

Those high-earning years coincide with the time when many adults take on new financial responsibilities such as a mortgage or child care or elder-care costs. While every generation complains about higher costs nibbling their earnings, millennials may feel particularly squeezed as they juggle a high debt-to-income ratio and try to catch up on delayed life events, according to data.

As of 2020, the median age of a first-time home buyer was 33 years old, up from 30 years old a decade ago, according to the National Association of Realtors. Americans are also waiting longer to become parents: According to the Centers for Disease Control and Prevention, the mean age of mothers at first birth was 27 in 2019, a record high.

Older millennials in their high-earning years are also still working to recover lost ground from previous bouts of unemployment or underemployment caused by the 2008 financial crisis, according to a 2020 study from the National Bureau of Economic Research.

“You carry that with you for a long time, maybe your whole career,” said William Gale, one of the authors of the study and a senior fellow in the Economic Studies program at the Brookings Institution.

The study—which examined household wealth across generations using data from the Survey of Consumer Finances, a survey conducted every three years by the Federal Reserve—found that the 2007-09 recession significantly reduced wealth for all age groups, and younger cohorts in particular. In 2016, millennial households held around 12% less wealth than did households headed by a person of the same age in 1989.

In 2019, the Federal Reserve Bank of St. Louis found older millennials’ debt-to-income ratios to be 23% higher than expected, based on previous generations at similar ages.

The overall real average wage of 2018 had the same purchasing power as it did 40 years ago, Drew DeSilver, a senior writer at Pew Research Center, wrote in an article. That means despite the strong gains in earnings and a growing post-pandemic labor market, many millennial households may not see more flexibility in their budgets, according to Mr. DeSilver.

“The extra money I make every year just gets funneled into something new, in the necessary things in our life,” said Andrea Pica, a 39-year-old working in pharmaceutical operations living in Neptune City, N.J. “Daycare is like a mortgage payment,” she added.

Ms. Pica began making more money in her mid-30s and recently crossed a personal income goal. She said the extra money she already made didn’t dramatically change her lifestyle. A large chunk of it instead went to pay for care for her first child, then her second.

According to the federal Consumer Price Index, the cost of child care and nursery school has risen at roughly twice the pace of inflation since 2000.

To some extent, financial stress might be a millennial generational trait. In a recent 2021 Deloitte poll of nearly 15,000 millennials and more than 8,200 members of Generation Z on the topics related to the pandemic and economy, 41% of millennials reported they feel stressed all or most of the time, and two-thirds of that group agreed their personal finance situations contribute to that worry.

Tim Eng, a 35-year-old product manager based in Colchester, Conn. said he, along with most of his friends and neighbors, has also reached personal salary goals, but those higher levels of pay also come with a cost: longer hours and more stress.

“You’re not going to make more income without drastically uprooting your life,” he said. “It’s about making the most of what they have.”

He said the pandemic has refocused his goals for this high-earning period. Now, Mr. Eng said he is aiming to save more money with the goal of one day retiring early or reducing his working hours so he can spend more time with his family.

Ms. Pica said she’s unsure how other people her age will handle rising housing and living costs.

“You just kind of have to figure it out, and I think sometimes that could even be an added level of stress, especially for those that are not high-income earners yet,” she said.

With plenty of job openings and baby boomers retiring, career advancement should help millennials with earnings. In the third quarter of 2020, about 28.6 million boomers reported they were out of the workforce because of retirement, according to a Pew Research Center analysis of monthly labor force data.

Yet labor-force participation for workers over 60 has increased, according to data from the Bureau of Labor Statistics. Boomers’ decisions to work longer could stall some career advancement for Gen X and millennial workers, said Mr. Ricketts of the Federal Reserve Bank of St. Louis.

“Gen X can’t move up to senior positions currently held by boomers, and then millennials can’t move up to their positions,” he said. “The broad numbers point to a challenge in that narrative that ‘baby boomers are done and setting sail on their boats to go fishing.’ This is really a story of folks still working.”

"Science and innovation are central to human progress and national economic success. Currently, the United States invests 2.8% of GDP in research and development, which is supported by a range of public policies. This paper asks whether the United States invests enough. To answer that question, the conceptual case for government intervention and skepticism about that case are reviewed. The paper then turns to systematic evidence, including the very latest evidence, regarding the operation of the science and innovation system and its social returns. This evidence suggests a clear answer: We massively underinvest in science and innovation, with implications for our standards of living, health, national competitiveness, and capacity to respond to crisis."

Benjamin Jones, "Science and Innovation: The Under-Fueled Engine of Prosperity," Aspen Economic Strategy Group, July 14, 2021, https://www.kellogg.northwestern.edu/faculty/jones-ben/htm/Science%20and%20Innovation%20_%20Underfueled%20Engine%20of%20Prosperity.pdf

Spending, “…United States has faced a slowdown in productivity growth and rising concern about the international competitiveness of the U.S. workforce over many years (e.g., Gordon 2012; Autor et al. 2013), where real wages for the median household have struggled to rise and failed to keep pace with the gains in prior generations (e.g., Council of Economic Advisers 2011; Autor et al. 2006). Yet, even as productivity has lagged, U.S. R&D intensity has slipped compared to other countries. In the mid-1990s, the United States was in the top five of countries globally in both total R&D expenditure as a share of GDP and public R&D expenditure as a share of GDP (Hourihan 2020). Today, the United States ranks 10th and 14th in these metrics, and U.S. public expenditure on R&D as a share of GDP is now at the lowest level in the last 60 years. See figure 5 for U.S. trends. By contrast, China has massively increased its science and innovation investments in pursuit of leading the world economically and strengthening its hand in global affairs. China’s R&D expenditure has grown 16% annually since the year 2000, compared to 3% annually in the United States. If China implements its current five-year plan, it will soon exceed the United States in total R&D expenditure….”

Range of estimates of social return to R&D spending

Allen Downey, "The Inspection Paradox is Everywhere," Towards Data Science, August 5, 2019, https://towardsdatascience.com/the-inspection-paradox-is-everywhere-2ef1c2e9d709

The Inspection Paradox is Everywhere

The inspection paradox is a statistical illusion you’ve probably never heard of. It’s a common source of confusion, an occasional cause of error, and an opportunity for clever experimental design.

And once you know about it, you see it everywhere.

How many students?

One of my favorite examples is the apparent paradox of class sizes. Suppose you ask college students how big their classes are and average the responses. The result might be 90. But if you ask the college for the average class size, they might say 35. It sounds like someone is lying, but they could both be right.

When you survey students, you oversample large classes: If there are 10 students in a class, you have 10 chances to sample that class; if there are 100 students, you have 100 chances. In general, if the class size is x, it will be overrepresented in the sample by a factor of x.

That’s not necessarily a mistake. If you want to quantify student experience, the average across students might be a more meaningful statistic than the average across classes. But you have to be clear about what you are measuring and how you report it.

By the way, I didn’t make up the numbers in this example. They come from data reported by Purdue University for undergraduate class sizes in the 2013-14 academic year.

From their report I estimate the actual distribution of class sizes (with guesses to fill missing data). Then I compute the “biased” distribution you would get by sampling students. Figure 1 shows the results.

Going the other way, if you are given the biased distribution, you can invert the process to estimate the actual distribution. You could use this strategy if the actual distribution is not available, or if it is easier to run the biased sampling process.

The same effect applies to passenger planes. Airlines complain that they are losing money because so many flights are nearly empty. At the same time, passengers complain that flying is miserable because planes are too full. They could both be right. When a flight is nearly empty, only a few passengers enjoy the extra space. But when a flight is full, many passengers feel the crunch.

Where’s my train?

The inspection paradox also happens when you are waiting for public transportation. Buses and trains are supposed to arrive at constant intervals, but in practice some intervals are longer than others.

With your luck, you might think you are more likely to arrive during a long interval. And you’re right: a random arrival is more likely to fall in a long interval because, well, it’s longer.

To quantify this effect, I collected data from the Red Line in Boston. Using the MBTA’s real-time data service, I recorded the arrival times for 70 trains between 4pm and 5pm over several days.

The shortest gap between trains was less than 3 minutes; the longest was more than 15. Figure 2 shows the actual distribution of time between trains, and the biased distribution that would be observed by passengers.

In this case the difference between the two distributions is moderate because the variance of the actual distribution is moderate. When variance is higher, as in the next example, the effect of the inspection paradox can be much bigger.

Are you popular?

In 1991, Scott Feld presented the “friendship paradox”: the observation that most people have fewer friends than their friends have. He studied real-life social networks, but the same effect appears in online networks: if you choose a random Facebook user and then choose one of their friends at random, the chance is about 80% that the friend has more friends.

The friendship paradox is a form of the inspection paradox. When you choose a random user, every user is equally likely. But when you choose one of their friends, you are more likely to choose someone with a lot of friends. Specifically, someone with x friends is overrepresented by a factor of x.

The difference is substantial: In this dataset, the average user has 44 friends; the average friend has 104, more than twice as many. And the probability that your friend is more popular than you is 76%.

Road rage

Some examples of the inspection paradox are more subtle. One of them occurred to me when I ran a 209-mile relay race in New Hampshire. I ran the sixth leg for my team, so when I started running, I jumped into the middle of the race. After a few miles I noticed something unusual: when I overtook slower runners, they were usually much slower; and when faster runners passed me, they were usually much faster.

At first I thought the distribution of runners was bimodal, with many slow runners, many fast runners, and few runners like me in the middle. Then I realized I was being fooled by the inspection paradox.

In long relay races, runners at different speeds end up spread over the course; if you stand at a random spot and watch runners go by, you see a representative sample of speeds. But if you jump into the race in the middle, the sample you see depends on your speed.

Whatever speed you run, you are more likely to pass slow runners, more likely to be overtaken by fast runners, and unlikely to see anyone running at the same speed as you. Specifically, the chance of seeing another runner is proportional to the difference between your speed and theirs.

We can simulate this effect using data from a conventional road race. Figure 4 shows the actual distribution of speeds from the James Joyce Ramble, a 10K race in Massachusetts. It also shows the biased distribution that would be seen by a runner at 7 mph.

Even if you are not a runner, you might have noticed the same effect on the highway. You are more likely to see drivers who go too fast or too slow, and less likely to see safe, reasonable drivers like yourself.

Orange you glad you asked?

A final example of the inspection paradox occurred to me when I read Orange is the New Black, a memoir by Piper Kerman, who spent 13 months in a federal prison. Kerman expresses surprise at the length of the sentences her fellow prisoners are serving. She is right to be surprised, but it turns out that she is the victim of not just an inhumane prison system, but also the inspection paradox.

If you arrive at a prison at a random time and choose a random prisoner, you are more likely to choose a prisoner with a long sentence. Once again, a prisoner with sentence x is oversampled by a factor of x.

But what happens if you observe a prison over an interval like 13 months? It works out that if your sentence is y, the chance of overlapping with a prisoner whose sentence is x is proportional to x + y.

Using data from the U.S. Federal Bureau of Prisons, I estimate the actual distribution of sentences for federal prisoners, as would be seen by a judge, the biased distribution as seen by a one-time visitor, and the partially-biased distribution seen by a prisoner with a 13-month sentence. Figure 5 shows the three distributions.

The distribution seen by a short-time prisoner is only modestly less biased than the view of a one-time visitor. The mean of the actual distribution is 3.6 years; the mean of the biased distribution is almost 13 years. To a 13-month observer, the mean is about 10 years.

The dataset I used for this example is a snapshot of the prison population in July 2019. So the reported distribution is biased; I had to “unbias” it to estimate the actual distribution.

Also, federal prisoners typically serve 85% of their nominal sentence. I took that into account in the calculations.

Had enough?

In summary, the inspection paradox appears in many domains, sometimes in subtle ways. If you are not aware of it, it can cause statistical errors and lead to invalid inferences. But in many cases it can be avoided, or even used deliberately as part of an experimental design.

You can read more about these examples in my books, which are available as free PDFs from Green Tea Press and also published by O’Reilly Media.

I discuss the class size example in Think Stats, 2nd Edition (affiliate link),