A K-Shaped Recover in Time? The COVID-19 Pandemic’s Effect on the Time-Spending Habits of the Rich and the Poor

Much has been made of the “shape” of the economic recovery in the wake of the COVID pandemic. Though the pandemic is (still) ongoing, the emerging narrative among economists and the data is that in many ways we experienced a K-shaped recovery through 2020 and 2021. This means that some - in this case higher-educated, higher-earning individuals who are typically able to work from home - had their income and wealth rebound and even quickly surpass pre-pandemic levels, while others - low-wage workers who may have lost their job in the recession or hold no financial investments - were stuck in decline or only partial recovery. The evidence in the employment data for this notion has been clear, while wage growth among low-wage jobs has actually been strengthening in recent months (though many of the recent wage gains have been erased by inflation). Both the stock market and unemployment rate underwent massive fluctuations in the wake of the March 2020 shutdowns. Government stimulus further complicated the inequality picture, providing significant but temporary relief to both the unemployed and middle-class Americans. But employment and the stock market aren’t the only way we can measure well-being or even economic impact. Another important measure is how individuals spend their time.

Source: FRED for employment numbers, Yahoo! Finance for S&P 500 Index

To examine statistically how Americans are spending their time, I want to turn to what I believe is one of the most interesting and unique US government datasets: the American Time Use Survey, or ATUS. The ATUS collects comprehensive information on thousands of individuals every month, ranging from demographic characteristics (age, race, location, etc.) to detailed minute-by-minute “time diaries” of how exactly they spent their previous day. If done correctly, we can summarize the ATUS data to get reasonable estimates of how different groups of people spend their time - such as how the average day varies by income (though there are still some potential issues in the data). To set aside discussion of how time-spending habits evolved over long periods of time, I’m going to restrict the analysis to the 2019 data. So I’ll be comparing the 2020 “COVID era” to the 2019 “pre-COVID era” (ATUS data for 2021 is not yet available).

The Broader Context

First off, let’s note that through the 2020 COVID recession, employment for high-income households remained fairly stable while low-income households spiked in both unemployment and out-of-the-labor force rates. While employment among low-income households remained below pre-COVID levels, the stock market boomed. And the households that tend to own significant amounts of stocks? High-income households.

One more note: income information in the ATUS was only collected for those who were employed at the time of the survey. Therefore when I group results by income levels, I’ll be missing those who were unemployed, which may bias the results. This is especially true for the low-income group since they were more likely to be unemployed through the COVID recession. So keep in mind as I present the comparisons that they are among those who were employed at the time of the survey and thus potentially not representative of the larger universe of Americans (which includes unemployed and those not in the labor force - at least 35% of adults).

Characteristics of the Rich and the Poor

To compare the time use habits of the “rich” and the “poor”, I need to define the actual compositions of these groups (at least for the purposes of this article). I took the weekly earnings variable, which was available for about half of the entire ATUS respondents sample, and multiplied it by 52 to generate a proxy of yearly income. This is an imperfect measurement of income since it assumes respondents earned income every week of the year, and the original earnings variable is missing for many people. Weekly earnings are also top coded at $150,000 to protect the privacy of high-earning respondents. So instead of relying on this income measure exactly, I’m going to place respondents into two bins: “low-income” if their projected yearly income is below $30,000 and “high-income” if it’s above $120,000. These amounts roughly correspond to the 25th and 75th household income percentiles in the U.S. in 2020. This still isn’t a perfect measure of economic status - it’s missing important dimensions of status like wealth and assets, it doesn’t account for the local cost of living, and who is missing income data likely isn’t random - so take that as a caveat for all below results. However, I think it does give us a rough capture of low-income and high-income status people in 2020 to compare against each other.

After weighing the sample to be representative of the entire U.S. population, my measure classifies about 30% of respondents as low-income and about 10% as high-income. Men are disproportionately high-income relative to women: while women make up nearly 63% of the low-income group, they are only 28% of the high-income group. High-income respondents are also on average 8 years older (46 vs. 38 years old), more likely to be Asian, and less likely to be Black than the low-income group. Among those employed at the time of the survey, 45% of low-income respondents were part-time workers, compared to only 3% of high-income respondents. Overall, the data shows these two groups are composed of significantly different types of people - this likely plays a significant role in how the pandemic shifted activities for the people in these groups in disparate ways.

Note: for building and grounds cleaning and maintenance, there were no respondents in the high-income tier that had that occupation, hence the thick single low-income bar.

Since we’re focused on the effect of COVID on time use, it’s important to note how the pandemic affected how work could actually be done. Of those who responded to the question, 58% of high-income workers were working remotely due to COVID-19, while only 14% of low-income workers were working remotely. On the other hand, only 6% of high-income workers were unable to work due to COVID-19 compared to 21% of low-income workers. The higher prevalence of remote work for higher-earning people, and the higher rate of pandemic-induced job loss, is both a reflection of the inequalities worsened by the pandemic as well as a driver in the time use trends that I will look at next. So before even looking into the time use data, we can already see how differently the pandemic affected everyday life for these two groups (and how different they were to start). 

Time, time, time - 2019 vs 2020

Okay, now that I’ve provided an armful of caveats and some contextual information, it’s time to dig into the actual time data. I’d like to compare how our income groups were spending their time in 2019 and 2020, before and then during the onset of the pandemic and remote work. ATUS collects information on over 250 activities, so I’m going to focus on several of what I deem to be the more interesting and important for the purposes of this article. The categories I include below represent over 90% of the total time in the day for each group and year. While there is likely interesting variation in many of the other, smaller categories, I’m going to stick to these representative categories. First, I’m going to compare our entire groups of rich and poor in these major activity categories.

Before looking at how time use diverged, we can already see the many ways these groups were different pre-pandemic. High-income respondents spent more of their days, on average, working and on recreation activities, while low-income respondents did more leisure activities. I’d like to again note that these are major activity categories that encompass all manner of actual tasks, so that labels like “leisure” or “traveling” should be interpreted loosely. Also of note is how working, traveling, and shopping time dropped for both groups - replaced by more personal care, leisure, and homecare activities. Only in sports/recreation/exercise do we see diverging trends in time use. So an initial look at the data actually provides potential evidence against divergence!

Next, we can look at how each group’s time-spending habits changed only among those that actually participated in those activities. For some categories - the ones in which basically everyone does at least a little of each day like sleeping or eating - this won’t change anything. But for others that vary on the external margin, this can provide a more comparable subset of people (such as those who are working or who participate in sports) to measure how our groups may diverge.

We now see that among those working through the pandemic, time spent on work dropped much more dramatically for the rich than the poor. While 30 minutes less of work may not seem like much, on the scale of millions of people reducing their working time every day, this can have massive economic effects. This is similarly true for the drops in traveling among the rich and in recreation among the poor - small shifts by an entire population could cause the rise or fall of certain industries. As I highlighted in a previous post, the changing habits of people when it comes to activities like eating out can doom businesses already operating on razor-thin margins. However, making any forecasts is premature even now, with how permanent these trends may be still an open question. As of April 2022, many companies are still grappling with whether to bring workers back to the office and for how many days a week!

One last chart I wanted to throw in is comparing the time use of rich and poor by the locations of where they spent their time. There aren’t too many surprises here - high-income respondents spend more of their time on airplanes while the low-income spend more on subways and bicycles, generally cheaper modes of transportation. The much higher amount of time spent in libraries and schools by low-income respondents is likely due to many students not working (or only working part-time jobs) while in school and thus falling into that low-income group.

Conclusion

It’s no secret that inequality has worsened in the U.S., a trend beginning at least as far back as the 1970s. The Great Recession was an exacerbator of this trend, as recessions tend to do, and the COVID recession may have further accelerated the growing divide. One key difference, however, is the government's response to these recessions. Most economists will agree that the federal government did a much better job of supporting its citizens following this most recent crisis. The eviction and student loan moratoriums, expanded unemployment benefits, and stimulus checks were among many policies that reduced the severity of the downturn and quickened recovery. This may in part explain the lack of divergence in time use habits as seen in the data above. Yet both the effect of the pandemic and the shape of the recovery remain to be seen. The U.S. continues to struggle with inflation and supply chain issues, and the threat of falling back into recession is non-negligible. Reversing the decades-long increase in inequality will also take much more than temporary relief programs. While the COVID pandemic certainly worsened inequality in many ways, it was not the start nor will it be the end of diverging circumstances and futures for the nation’s rich and poor.

Final Notes

All facts and figures in this post were created from weighted ATUS data. Weights used come from the WT20 variable in the IPUMS data. As their data description notes, “WT20 does not yield annual estimates. It is designed to provide estimates that are representative of the period from January 1 through March 17 and May 10 through December 31. This weight omits the March 18 to May 9 period because 2020 data were not collected on these days due to the COVID-19 pandemic. This weight is required for analyses that include 2020 data.”

ATUS data: https://www.bls.gov/tus/database.htm

IPUMS Citation: Sandra L. Hofferth, Sarah M. Flood, Matthew Sobek and Daniel Backman. American Time Use Survey Data Extract Builder: Version 2.8 [dataset]. College Park, MD: University of Maryland and Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D060.V2.8Â

Charts seen in this post were made in R using the tidyverse, readxl, and ggthemes, directlabels, and RColorBrewer packages. Data was downloaded from IPUMS and cleaned using Stata.

In the future, I’d like to revisit this post with two extensions: delve more into the subcategories of time use and see in more detail how the rich and poor vary their activities at a more granular level, and try out a matching procedure to pair rich and poor on dimensions of education, age, race, etc. The latter method would allow for a (potentially) causal comparison of the two groups’ time usage and may provide more interesting insight into how otherwise-similar people diverge in their daily habits on the basis of income. These were my original plans for this post but I had to stop short as personal matters got in the way - but I hope to return to it when there is more data later on!

If you have questions or constructive feedback, feel free to email me at troded24@gmail.com, submit an inquiry on this website, or leave a comment on this post! Thanks for reading.

The CEO Investment Strategy: Part I

This has been the most fun I’ve had working on a post so far. Well, maybe that’s not fair to say since this is only my second project post. But still, this was a lot of fun to work on. So what was this project about? Let’s begin with some backstory.

Personal investing today is easier than it has ever been. The popularization of low-cost, no-fee passive investing offerings - such as Vanguard’s mutual funds - made it easy to throw your money into an account and forget about it. More recently, this low-cost low-management trend has entered the active investing market as well. Perhaps most popular among the young adult crowd, certainly among my peer group, has been the Robinhood app. Robinhood let’s anyone trade on the stock market, given you have a bank account to deposit money from and a social security number. It charges no fees, requires no minimum deposit, and has in recent months introduced cryptocurrency trading, option trading, and extended market hours. Robinhood’s business model could be an interesting post by itself, but let’s save that for another time. The point is, most anyone with a couple bucks to spare nowadays can jump into the market and begin trading pretty much immediately.

So with the proliferation of traders so too has the number of trading strategies grown rapidly. Investment strategies act as guides [for] an investor's actions with respect to asset allocation. Strategies vary, but they are based on individual goals, risk tolerance and future needs for capital. To put it another way, investment strategies are a simple set of rules an investor follows based on their personal parameters such as their market sector of interest, timeline, amount of risk they are willing to accept, and tons of other variables. Traditional strategies have varied from simple rules such as “technology sector” or “energy” stocks-focused portfolios, to more complicated principles involving market cap size, asset betas, and other financial measures. More recently we have seen strategies pop up such as “female CEO’s” or “environmentally-friendly companies” - strategies often clearly aimed toward drawing in millenial’s interests. While it’s hard to vouch for the success of these strategies, it’s definitely an appealing idea. Based off your passions or simple likes/dislikes, devise a simple set of rules for where to put your money. Follow these rules and there you have it, your investment strategy! All the stress and worry of financial management reduced to a simple binary choice: does this stock fall into my the bounds set by my rule. Yes? Buy that stock. No? Move on.

Investment strategies, especially the way they have evolved to accommodate the changing capital market and entry of millennial investors more recently, is something I plan to continue researching and hopefully make several posts about. To begin this topic, however, I want to explore the simple idea of breaking stock performance into categories or segments based off some rule. So let’s devise a simple ‘dummy’ investment strategy. What if we invest based on the CEO of a company? Which CEOs would have made us the most money? How have certain stocks performed under successive CEOs? Note that I’m not actually advocating to determine your investing strategy solely by the CEO of a company - that is a way oversimplified method. I’m also not suggesting that any company’s stock performed better or worse strictly because of it’s CEO - again, that would be an oversimplification. At its root, I’d like this post to simply be a straightforward look at historical stock performance partitioned by company leadership.

At this point I think I’ve written way too much without showing any data if I want to keep calling this website “data-driven”. So let’s toss up a chart.

What a mess. But a good starting point! So here we have 17 different stocks, spanning a broad range of the market - financial, technology, industrial, and food sectors. I attempted to choose companies across a variety of industries that were also some of the top performers in those categories. We also have an indicator for the entire market, the S&P 500 Index, which I pulled from the ticker “^GSPC” on Yahoo Finance. This will serve as our baseline for performance as we examine each individual stock. Using the S&P 500 for comparison allows us to reduce the effect of systematic risk on the stocks we observe. That is, events that caused the majority of stocks to fall, are not caused by a single company’s actions alone, and are nearly impossible to avoid. One example of a systematic risk would be the trade war ongoing between the US and China - not caused by any company but influencing the stock price of many. Our interest is on how the individual stocks we will analyze performed relative to the market as a whole. So instead of observing a stock’s performance compared to zero, we will look at how a stock performed relative to the S&P 500 Index. Thus if a systematic even occurs, this shock will apply to the broader market and will be reflected in the S&P 500.

Another important note before jumping into the charts is my methodology. Rather than look at absolute performance of these stocks I chose to index them. This is important! To gather this data I went onto Yahoo Finance and pulled the monthly closing price for each stock. I then indexed the stock performance by dividing each subsequent monthly datapoint by a base datapoint - the first available observation of historical performance. This was done for each individual company. I then indexed our S&P 500 indicator by choosing that company’s first available month as the base point for the S&P 500 as well. So our y-axis is measuring not the actual closing price but the growth rate from the first date of the stock price to today. Thus the y-axis is labeled “Index Value”, and is derived from the closing price of the relevant stock. With that said, let’s begin!

The Tech Sector

At first glance this chart appears to be saying that Cisco (in purple) is much larger than Google (in orange), a fact that is clearly incorrect. Remember, we are looking at indexed values where we are comparing a company’s stock price today relative to its historical price, indexed at 1 from it’s first-ever closing price. So Cisco is much higher on this chart than Google because Cisco first opened at a share price of $0.08 [NOTE: this is the historically adjusted price, accounting for stock splits] and is today at $43.75 (a 56,720% increase) while Google opened at a share price of $50.85 [NOTE: again, historically adjusted price] and is today at $1,235 (a 2,273% increase). In actual value, Google has a market cap of about $840 billion compared to Cisco’s market cap of about $215 billion. Google is nearly 4x larger. So my method of indexing values makes it useful for us to compare time-series data on each company, but not very accurate for comparing across companies. This is fine since the goal of this project was to look at company’s performance over time, not to compare different companies’ performances to each other. I probably shouldn’t even include the charts that compare across companies like this tech sector one, but I think we can still find useful information from them as long as we keep in mind this disclaimer. I’ll make sure to keep bringing it up as we explore the data so that no chart is mistaken in its meaning.

Apple’s growth is tremendous, making the S&P 500 Index look like it has barely grown by comparison. The struggles of the company pre-Steve Jobs era are apparent, as Apple was actually being outperformed by the market until the reveal of the iPhone. After that there was no looking back, and under Tim Cook Apple has become the most valuable company in the world (just recently becoming the first to reach a $1 trillion market cap).

There’s really not much to say here. Amazon is the best possible public company to have invested in for the 21st century. The growth of Amazon’s stock price, especially since 2012, has redefined the phrase “the sky is the limit”. It’s made the growth of the S&P 500 look like a flatline by comparison. With Jeff Bezos at the helm, Amazon has achieved the highest index value of this dataset. In just over a decade and a half it has grown over 1200x it’s original price. Let that sink in. If/when Jeff Bezos steps down, the CEO chosen to fill his shoes may have an impossible task ahead of them.

Comcast is another company that has undergone impressive growth post-recession. For its entire existence it has been managed by the Roberts family, current CEO Brian being the son of founder and original CEO Ralph. Following a long period of turbulence from 2000 to 2010, Comcast found its footing and expanded into the (perhaps not very-liked) corporate machine at the top of the media world today.

Cisco is another tech company that experienced explosive growth. In fact, it’s 2001 peak, nearly hitting 1000x the stock price since it’s open, is among the highest index value of all the data I collected. The subsequent pop of the dot-com bubble brought it back down to earth, but under CEO Chuck Robbins, Cisco has maintained a very appealing growth rate.

In terms of time being publicly traded, Google (now known as Alphabet, Inc.) is a very young company. As a result its index value is a bit lower compared to some of the other tech companies, but you have to keep in mind the short time frame. 25x growth since 2004 is spectacular, and when accounting for its actual market cap the real size of Google is revealed. Relative to the S&P 500 which grew only about 3x in that same time period also provides a more impressive indicator of Google’s dominance.

IBM has been around for a long time, especially for a tech company. Since 1962 it has gone through 8 different CEOs and in those 56 years experienced mixed growth rates. Hard times recently have been plaguing IBM’s growth as the S&P 500 has widened the gap in performance.

Intel is another company that displays the impact of the early 2000s dot-com bubble. Like Cisco it grew incredibly fast and incredibly large, crashing just as quickly as it rose and has not quite yet reached that 2001 level. Still, under CEO Brian Krzanich’s guidance the company achieved excellent growth. It’s index value practically doubled in less than 5 years! With new CEO Bob Swan just starting earlier this year, it remains to be seen if Intel can sustain that growth rate

Last among the tech stocks is Microsoft. Microsoft’s chart also perhaps provides the clearest indication of a connection between CEO and stock performance. Under founder Bill Gates, Microsoft exploded (although also helped along by the dot-com ubble) and hit the 600x growth mark at the turn of the century. When Steve Ballmer took over this growth floundered, unable to break out of the 300x range. But current CEO Satya Nadella has helped Microsoft rediscover that Gates-era magic and reach new highs. As a result Microsoft is one of the best performing companies by growth, only getting beaten by Amazon.

Next up is the financial sector, but seeing as how long this post has already gotten, we’ll save it for Part II. To be continued…

Final Comments

All historical stock performance data was pulled from yahoo.finance.com. Performance was pulled as monthly data, selecting for closing price. Dates for CEOs were found using simple Google searches, and so may be slightly inaccurate. Start and end dates pulled from official company website where available and found.

Data was then imported in STATA, where I cleaned it and merged in S&P 500 Index data. Values were then indexed to oldest closing date and using a simple growth formula [(closing price at date N)/(closing price at date 1)].

The datasets were then exported and brought into R. All visuals were made in R, using the following packages: readr, ggplot2, dplyr, RColorBrewer, ggthemes, tidyverse, stringr. I styled my charts after one of my favorite websites, FiveThirtyEight, using the ggtheme of the same name.

You may have also noticed that the line colors for each company are that company’s logo colors! Credit to http://www.codeofcolors.com/brand-colors.html for providing the hex color codes for each company. Also credit to http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf and colorbrewer2.org for providing additional color schemes and R color information.

Additional parts featuring other sectors will be follow soon! If there are any companies you'd like me to take a look at, leave a comment, shoot me an email, or fill out the inquiry form on this website.