Two new studies on who works from home, and its mental health impacts

The pandemic caused a massive rise in working from home and now, even though lockdowns are long since over and many workers have returned to the workplace, we are beginning to understand working from home (WFH) a lot better. Two new studies have recently added to our understanding.

The first is this article by Cevat Giray Aksoy (European Bank for Reconstruction and Development) and co-authors, published in the AEA Papers and Proceedings (ungated earlier version here). They use data from the monthly US Survey of Working Arrangements and Attitudes, limiting their data to the period from January 2024 to December 2025, and document three facts about WFH. First, employees are more likely to work from home if they work for a younger firm, and peaks among those working for employers that were founded in the height of the pandemic, in 2020.

Second, employees are more likely to work from home if they work at a firm with a younger CEO. Specifically:

Firms led by CEOs under 30 have an average of 1.4 WFH days per week, compared with 1.1 days at firms led by CEOs who are 60 or older.

That doesn't seem like a lot, but an additional 0.3 days per week is a little more than three working weeks per year of WFH for those working for the youngest CEOs compared with those working for the oldest. However, this relationship between CEO age and WFH appears to be partly explained by the fact that younger CEOs are more likely to be leading younger firms. When Aksoy et al. put both CEO age and firm age in the same regression model, only firm age remains statistically significant. It is a similar story for CEO gender, which is initially statistically significant, but since female CEOs tend to be younger and to be CEOs of younger firms, CEO gender isn't statistically significant once those other variables are controlled for.

Third, the self-employed are much more likely to work from home. Specifically:

Self-employed workers report two to three times as many WFH days per week as wage and salary employees, depending on employer size. Compared to wage and salary employees, the self-employed are more than three times as likely to work in a fully remote capacity.

This last result is not entirely surprising, given that the self-employed typically have a lot more flexibility over scheduling. And, the self-employed may be the type of people who most value flexibility as well.

The second new article is this one by Natalia Emanuel (Federal Reserve Bank of New York), Emma Harrington (University of Virginia), and Amanda Pallais (Harvard University), published in the prestigious journal Science (open access). They look at the mental health impacts of WFH, using US data from a variety of sources, and a difference-in-differences approach. This involves comparing occupations that are more or less amenable to WFH, between the time before the pandemic and the time after the pandemic. They refer to the occupations that are more amenable to WFH as 'remotable'.

Emanuel et al. first document the dramatic rise of WFH:

The pandemic led to a large increase in remote work for those in remotable jobs, such that by 2024, workers in remotable jobs spent 31.1% of workdays fully remote, whereas people in nonremotable jobs spent only 8.9% fully remote... Those in remotable jobs experienced a 17.9 percentage point (pp) differential increase in fully remote work...

They then show that this rise is associated with more time spent alone:

Along with spending less time in the office, workers in remotable jobs spent more time working alone after the pandemic, logging 1.2 more work hours alone per day relative to nonremotable workers (58.0% increase; P < 0.0001).

Even for those of us who are introverts, more alone time may not necessarily be a good thing. Emanuel et al. are concerned about how WFH and working alone affects mental health. Their main outcome variable is the Kessler (K-6) Psychological Distress Scale, which is:

...based on how often in the past 30 days the respondent felt worthless, hopeless, restless, nervous, that everything is an effort, or so sad that nothing could cheer them up...

Their main source of data is the Panel Study of Income Dynamics covering the period from 2011 to 2023 (from which they exclude the pandemic years 2020 and 2021). Analysing that data, they find that:

Between the pre-and postpandemic periods, mental distress increased for everyone, but it increased significantly more for those in remotable jobs...

Among those in remotable jobs, there was a 0.3 unit increase in the K-6 distress score relative to an average score of 3.0 before the pandemic (standard deviation change = 0.08; P = 0.063) in the Panel Study of Income Dynamics (PSID). In the National Health Interview Study (NHIS), we found the same 0.3 unit deterioration (P = 0.007). We saw deterioration in each of the six subcomponents of the K-6 distress scale: feeling worthless, hopeless, restless, nervous, that everything is an effort, and so sad that nothing can cheer them up...

Importantly, the deterioration in mental health is concentrated among people living alone, which is consistent with the idea that WFH affects mental health through increasing social isolation. Emanuel et al. also find that people in remotable jobs are more likely to seek help from a mental health practitioner, and take relatively more prescription medications for mental health conditions such as anxiety or depression. These changes aren't simply the result of greater flexibility allowing more time to be devoted to health care generally, as there was no change in visits to the doctor and no change for other prescription medications such as statins.

Finally, Emanuel et al. looked at whether the rise of generative AI, rather than the increase in WFH, might explain the results (an important check, given the paper I will blog about tomorrow). They find that results from the same analysis, but substituting an AI occupational exposure index in place of the 'remotability' index, are not statistically significant.

Now, many workers are very keen on WFH - as noted in this post, about half of Australian workers would be willing to give up some salary in order to work from home. Why would people choose more WFH if it may worsen their mental health? Of course, a rational worker would weigh up the benefits and costs of WFH, and may decide that the mental health costs are more than offset by other benefits. However, Emanuel et al. point to another related possibility, which is:

...that the benefits of remote work (e.g., skipping a daily commute) are immediate and salient, whereas the costs of remote work (e.g., frayed connections with co-workers) take time to materialize.

So, a rational worker may be essentially weighing up benefits that occur today, against uncertain costs that may occur sometime in the future and therefore should be discounted (in the same way that we should discount future cashflows in a financial analysis). In that sort of exercise, where the mental health costs are discounted, it is more likely that workers would choose to work from home. They would be even more likely to do so if they are quasi-rational and heavily discount the future, as I note in the first week of my ECONS102 class. In that case, the mental health costs would be heavily discounted. Finally, maybe workers are simply unaware of the mental health costs of WFH. If that is the case, then an information intervention might be helpful in improving mental health among workers who would otherwise be WFH. In the meantime, this research suggests that the post-pandemic rise in WFH may have contributed to some part of the growing mental health crisis, especially through increased time spent alone.

[HT: Marginal Revolution for the Emanuel et al. article]

Read more:

• Remote work, wages, and compensating differentials
• Willingness-to-pay for working from home

Maybe hosting the Olympics just shuffles income around a country, rather than increasing it

There is a large, and still growing, literature on the economic impact of large sporting events (see this post, and the links at the end of it, for some examples). My conclusion from that body of research is that large sporting events are expected to generate large economic impacts (based on studies conducted before the event), but generally the actual economic effects are small or non-existent (when measured after the event). However, the studies are typically based on a single event, or a small number of events. Are the typical null results driven by a small sample size and if so, would a larger and more diverse sample demonstrate different results?

That is the question essentially underlying this 2021 article by Matthias Firgo (Austrian Institute of Economic Research), published in the journal Regional Science and Urban Economics (ungated earlier version here). Firgo looks at the effect of the Olympic Games (both summer and winter) on regional GDP per capita in the host region (not GDP per capita in the whole host country, or only in the host city), using data from the 1992 Winter Olympics in Albertville to the 2020 Summer Olympics in Tokyo. Importantly, Firgo uses a control group made up of regions with cities that had been shortlisted by the International Olympic Committee (IOC) to host in the same year, but were unsuccessful (more on that a bit later).

Because of data limitations, Firgo focuses on GDP per capita as a percentage of national GDP per capita - essentially a relative measure of wellbeing at the regional level. Using this measure, he finds that for the Summer Olympics:

...regional per capita GDP significantly increases by 3.6 %-points (3.3 %-points) relative to national per capita GDP in the year of the event (the year before the event).

In other words, the host region’s GDP per capita rises by around 3 to 4 percentage points relative to national GDP per capita in the lead-up to the event. In contrast, there is only very weak evidence of any persistent effect of the event on regional GDP per capita, and the Winter Olympics (which are a smaller event, and typically held in smaller cities) had no significant effects. The positive effect of the Summer Olympics on regional GDP per capita in the years immediately before the event is consistent with increasing spending on infrastructure (including sporting, transport, hospitality, and cultural infrastructure) in the lead-up to a substantial event. That there is no persistent effect is fairly consistent with the other research on the economic impact of large events.

However, there are two other things to take away from this research. First, if anything these results might overstate the impact of successfully bidding for the Olympics. Whether a potential host city's bid is successful or not is not a random event. Cities that are more likely to be successful hosts should, at least in theory, be more likely to be selected as hosts. So, the control group is an imperfect comparator for the treatment cities in a way that is likely to bias the results. If successful hosts were cities that the IOC believed were already on an upward trajectory at the time of the Olympics, then that would bias upwards the estimated impact of the event. Of course, such foresight from the IOC would have to be executed seven years before the event (which is when the hosts are typically selected), but nevertheless there is potential for upward bias. That said, shortlisted cities are still likely to be a better comparison group than all non-host cities, since they had already demonstrated some capacity and willingness to host.

Second, these results tell us more about relative effects within the host country, rather than absolute economic impacts. They show that the GDP per capita increases in the host region relative to the rest of the country. Given that the overall economic impact is small to negligible, as are population changes arising around the event (both of which many other studies have shown), a large part of the relative increase in GDP per capita in the host region must arise from a combination of increased GDP per capita in the host region, and decreased GDP per capita in other regions in the same country. Effectively then, hosting the Summer Olympic Games simply shuffles income around a country in the lead-up to the games, with the host region benefitting while other regions are negatively impacted. Then after the event, there is a return to the normal inter-regional distribution of incomes.

The Olympic Games is a large spectacle - an opportunity for national celebration as we watch sporting heroes compete to win medals. The evidence still suggests that the Games are not a source of sustained economic growth, and that any short-run gains may be highly localised rather than national, and some of those gains come at the expense of other regions.

Read more:

• Tim Hazeldine on economic impact studies
• Beware economists bearing impact studies of the America's Cup
• Even less reason to believe in the economic impact of the America's Cup
• The overstated impact of the Super Bowl
• The economic impact of the 2010 World Cup in South Africa
• The economic impact of the 2000 Sydney Olympics

Book review: The Nvidia Way

The biggest news story about stock markets over the last three years has probably been the dramatic rise of technology stocks, and particularly those related to AI. And among those stocks, one of the standout performers has been computer chip maker Nvidia. The success of Nvidia now hides the fact that the company had many close calls, where it was literally on the verge of closing down. That is one of the key facts that I learned from reading The Nvidia Way, by Tae Kim.

Kim was previously a technology columnist at Bloomberg, and he tells us he wrote several comments critical of Nvidia. Nevertheless, Nvidia allowed him to have unprecedented access to Nvidia staff, but more importantly, to CEO Jensen Huang. And that is important, because the story of Nvidia, and 'the Nvidia way' is undeniably a story of Jensen Huang. Huang wasn't the only founder of Nvidia, but he has been the face of the company, the driving force behind its successes, and the person most responsible for picking up the pieces after its frequent failures. Kim writes that:

In all my years covering business, as a consultant, an analyst, and now as a business writer, I have never met anyone quite like Jensen. In the field of graphics, he is a pioneer. In the harsh technology market, he is a survivor. And he has been a CEO for more than thirty years - marking him, as of this writing, the fourth-longest currently-serving CEO in the S&P 500...

Kim clearly has a lot of respect for Huang, and this shines through the whole book. Even where other authors would press on the more negative aspects of Huang's personality, such as his ultra-competitive nature, Kim is more measured:

Jensen was so competitive that he challenged other employees even when he was at a disadvantage. In high school, CFO Geoff Ribar had ranked among the top fifty chess players in the country. His boss, however, would not accept that someone else was better than him...

Jensen attempted to close the gap between his and Ribar's chess skills through brute-force learning. He memorized chess openings and sequences of moves, so that he would control the board. Yet Ribar round his playing style predicable... Every time he lost, Jensen would swipe his arm across the board, knocking over the pieces, and storm away. He would sometimes later insist on a rematch on the ping-pong table. Ribar graciously accepted, knowing Jensen was purposely shifting the competition onto more favorable territory.

It is worth noting that Huang was a champion table tennis player. His competitiveness has clearly served him well in business, and is one of the key factors in Nvidia's success.

So, what is 'the Nvidia way', after which the book is titled? Kim notes that it has several characteristics, including the hiring raw talent especially through aggressive hiring methods, its emphasis on retaining high-quality employees, its strong focus on a culture of excellence, the high demands it in turn places on those employees, and the leadership of Huang himself. Not all of these characteristics, especially not Huang, could necessarily be replicated at other companies. However, there is a lot that budding leaders could nevertheless learn from this book.

Having said that, there is one element where the book could have explored deeper. There were many occasions where Nvidia was close to failure, including following the release of one of its very first chips. Obviously, Nvidia is wildly successful as a company now. But should we interpret the company's success in spite of its challenges as the result of good management, culture, and hard work, or should it be interpreted as luck? In other words, how much of Nvidia's observed success is simply survivor bias? Kim obviously sides with attributing the company's success to its own good efforts, but it would have been good for him to turn a more critical eye to just how lucky they had been at key points.

Despite that gripe, I really enjoyed this book. I distinctly remember buying an Nvidia GEForce graphics card many years ago. Kim does a great job of bringing to life all of the characters and their contributions to the story, as well as the key events in the life of the company. If you're interested in understanding the rise of Nvidia, this book is recommended.

This week in research #129

Here's what caught my eye in research over the past week:

• Araya et al. (with ungated earlier version here, but in Spanish) evaluate the impact of using the CORE textbook (which I use in my ECONS101 class) in introductory microeconomics in Uruguay, in comparison with a conventional textbook, finding no systematic differences in pass or dropout rates between the two courses, but that students using CORE are significantly more likely to believe that it contributed to their academic and professional development
• Baker et al. (with ungated earlier version here) study the staggered rollout of unionisation across Canadian universities between 1970 and 2022, and find that unionisation compressed salaries, with wages at the bottom of the unconditional distribution increasing by roughly 10 percent, while wages at the top were unaffected
• Baker et al. (but a different Baker, and with ungated earlier version here) provide a detailed summary of different types of difference-in-differences (DiD) research designs and their associated estimators, as well as discussing covariates, weights, handling multiple periods, and staggered treatment (this will be a highly cited resource, given the number of studies that use DiD for causal inference)