The Returns to Leisure, Part 2

(See Part 1 here.)

My Data

I’ve finished my two-week experiment in logging my leisure time, and here are the final results:

ActivityTime (hours)Cost Incurred During Tracking PeriodTotal Fixed Costs To DateFixed Recurring CostsApproximate Cost Per Hour Since Fixed Costs IncurredApproximate Cost Per Hour w/ 2-year Asset Life
Eating Out1:43$46.20n/an/a$5.67n/a
TV and Movies9:47$0.00$211.49$176.00 / year$3.78$0.75
Video Games54:28$42.17$401.83$59.99 / year$5.28$1.53
Web Surfing / Blogging2:00$0.00$69.98$29.99 / month$3.50$0.67

Calculating costs has been the most difficult part of this. Each of these activities has a different cost structure that, depending on how I try to calculate it, gives me a very different picture of how costly the activity is. Photography, for example, appears very expensive. And the equipment is expensive. But the marginal cost is zero to go outside and take pictures. The cost per hour will continue to go down as I take pictures (which I’ll likely do more in the summer than during these winter weeks).

To incorporate fixed costs in a reasonably way without knowing the life of the assets, I included two measures. First, I divided the fixed costs by the days since the cost was incurred and multiplied that by average hours used per day. That gives me an estimate of how much the assets cost for each hour of use to date. This biases older assets, but thankfully most of the equipment used was purchased within a month or two of each other. The camera equipment was oldest, but so expensive that the bias didn’t do much to make it seem cheaper. Second, I just assumed that all assets have a life of two years. This removes the time-since-purchase bias, but includes an arbitrary assumption that I’ll be using this stuff for two years exactly. For all I know, my TV blows up and I need to buy another tomorrow while my camera lasts for a decade.

Some activities, like watching Netflix or paying for Internet, have recurring fixed costs. This makes these activities look more or less expensive depending on how I choose to calculate them. I decided to use the data I collected to divide cost per pay period by the estimated average hours of use per pay period, and add it to the division of non-recurring fixed costs incurred over ownership time to date.

After calculating all of the costs, I realize that they barely had any impact on my decisions. Since most of the costs were sunk and marginal costs were zero, I just did whatever I felt like doing most with little concern for expenses.

I would have preferred to make this in D3, but I couldn't be bothered...

The number one activity, as I already mentioned, was video gaming. It had the second highest hourly cost, but it’s unclear if this would hold over a longer time span. I bought several games during the past two weeks, and playing those games rather than purchasing new ones could push the hourly costs lower.

The most expensive activity by hour was eating out. This isn’t surprising, since it’s the activity that consumes physical resources and carries a high labor cost.

Some notes from recording

Web surfing is probably underreported, since I mostly did it in small 3-5 minutes chunks between other activities that I didn’t bother to record. I also didn’t record any mindless surfing I might have done at work.

Also, I apparently didn’t record anything for 3/28/2014. This is almost certainly an oversight on my part.

Lessons Learned

First, it’s really annoying to keep accurate records of this stuff. Not any worse than other things I track like diet, but still a pain. I want to repeat this experiment in a few months to see if a change in season has an effect, but I won’t be continuing this data collection for now.

As I discussed in my last post, I’m clearly addicted to video games. But the time I spent playing them did decrease during the second week, which might be attributable to being mindful of time spent through measuring. Either that, or it’s because I wasted hours of my life last Saturday filing my taxes. Could go either way.

Video games have a small hourly cost due to the huge number of hours I put in. But that ignores the opportunity cost. Most of my gaming binges happened on the weekends, which would be the best time to do freelance work. I’ve arbitrarily set the opportunity cost of leisure at $50, which was the last hourly rate I charged on a freelance job. Assuming I could actually get enough freelance work to fill the same number of hours spent on leisure (unlikely, but let’s pretend), then video games had an opportunity cost of $2,723.44. That’s over $70k per year in opportunity costs. Yikes!

Again, too lazy for D3 right now.

Whether or not I could actually be earning another $70k/year if I gave up video games is disputable. Obviously I don’t always have other paying work to do outside regular office hours. And even if I did, I wouldn’t always take it–I clearly value leisure rather highly. But putting these numbers into some slightly arbitrary context still serves a good purpose, if not the one I originally intended: it’s a kick in the nuts to cut back on video games and start freelancing more.


I’ve heavily edited this piece to reflect changes in cost calculation methodology. I’ve tried to be more uniform in my cost calculations across very different types of activities, and it significantly changed my cost data.

This is how the costs were previously displayed:

ActivityTime (hours)Total CostHourly Cost
Eating Out1:43$46.20$26.91
TV and Movies9:47$0.61$0.06
Video Games54:28:00$42.17$0.77
Web Surfing1:11$0.05$0.04
  1. Blogging: Cost of Internet connection by the hour.
  2. Eating Out: Cost of all meals with my wife, divided by time we spent eating.
  3. Photography: Cost of all photography equipment divided by hours we’ve owned it. An imperfect measure, but better than none.
  4. TV and Movies: Hourly cost of Internet, plus hourly cost of Amazon Prime and Netflix.
  5. Video Games: Cost of all games purchased plus hourly cost of Xbox Live.
  6. Web surfing: Cost of Internet connection, calculated hourly.