# Cook or Order Takeaway? How Employment Status & Pay Level Changes How Much You Value Your Time.

## Data analysis of over 13k responses to the Value of Time Calculator.

Jan 6 · 8 min read

Cook dinner myself for cheap, or order a takeaway?

You face these questions every day. And, whether consciously or not, you do make decisions about them. But, how do you know that the choices you make are good?

The only way to know would be to quantify how much an hour of your time is worth for you. Luckily, there is a tool that helps you quantify that. And, based on the responses people share in it, we can learn a lot more about the relationship between work and the value of our time.

## What? A Value of Time Calculator? 😯

The Value of Time Calculator (VTC) is a tool developed by Clearer Thinking, a research organisation whose “

The VTC is an interactive online quiz that faces respondents with diffrent scenarios and thought-provoking questions. It takes about 10 minutes to complete and, after answering all the questions, you get a fairly unbiased estimate of how much you value their time. ()

The tool also asks many demographic questions too that can offer further insights into how people relate to their time.

I was lucky to get connected with Spencer, the founder of Clearer Thinking, and had the opportunity to work on a small data project related to VTC.

## 📊 What is this data set?

The dataset was a slice of the full dataset, and included 13958 (anonymised) usages of the program. It was 13958 rows and, brace yourself, 423 columns. You can imagine, a lot of cleaning was needed.

I’ll spare you the details of the process, but you can be sure that the prep has included:

• removing NaNs and null values ()
• identifying outliers (not as easy as you’d think [or, rather not as easy 😅], given that the answers were given in different currencies)
• standardising the numbers, that is, converting all currencies into international dollar.

Currencies = location?

On the last point, the dataset had information about the currency used by the respondent, which we could use as a proxy for their location. However, I’ll be mostly avoiding referring to respondents currency, without assuming location. This is for precision and to avoid sounding like I’m making far-reaching conclusions. If you want you can, in your head, swap USD earners for North Americans, INS earners for Israelis. etc.

Statistical significance

The analysis involves making comparisons between different groups of respondents. Most of the time the groups were not of equal size and, in several cases, not large enough to be able to make observations with confidence. There are further notes on this in the text.

⬆️ This is the boundary. ⬆️

It marks the point where, all the boring bits and disclaimer are out of the way!

Let’s get to what we’ve found.

# 🕵️ How does employment status change how you value your time?

As a part of the test, users answered a question regarding their employment status. It’s natural to wonder then:

On average, across the dataset, the employed value an hour of their time higher than the unemployed—at \$39.24 and \$21.82 respectively. The same group gave higher values in the answers to each question in the VTC tool.

For ease of interpretation, and to boast my matplot skills, we can plot this.

Output:

Another question regarding the employed vs. unemployed dimension is checking whether there are any differences across currencies. For example:

We can see the mean values, by grouping by currency (‘symbol’) and employment.

Value of time (column ) is what we’re most interested in, so let’s plot.

Looks great, as always Marta—supreme plotting skills right there 💁‍♀️—but what does it all mean?

After running t-tests between all the different groups, it turns out that:

• The difference in how employed people value their time is significant when we compare the employed USD earners with the employed GBP, INR, and NIS earners.
• The difference is not significant between USD and EUR, and USD and RUB.

In other words, employed USD earners assign a significantly higher value to their time than those who earn in GBP, INR, and NIS.

☝️ We need to bear in mind that some of the sample sizes were much smaller than others (e.g. 5985 employed USD earners, vs. 130 employed NIS earners). So, in terms of the results above, I’d be most confident regarding the difference between USD and GBP.

For all other pairs of currencies the differences were statistically significant, except for one: EUR and USD. This means: while USD earners, on average, value their time slightly higher (at than EUR earners (at ) , the difference is not significant.

# 💵 How do earnings influence the final value of time?

We also have data on the total number of hours work per year the respondents work and their total annual wages. This means we can calculate an average pay per hour. Here are the mean values, by currency, dollar per hour in the right most column ().

Below are three charts to visually represent mean wage, mean earnings per hours, and the mean number of hours worked per year.

There is one basic observations we can make based n this such as: it looks like INR and NIS earners (India and Israel) work the highest number of hours and earn the least per hour.

But, there is a better question to address: How does these numbers square with how the respondents from different currency groups value their time?

If you earn more per hour does it mean you’d value your time more?

I calculated the ratios of mean value of time to earnings per hour for each currency. In ascending order they are: INR: 1.11, USD:1.48, GBP:1.57, NIS:1.75, EUR:1.8. RUB removed due to insufficient data.

USD earners have the highest annual wage and the highest pay per hour. But, it’s the people who earn in other currencies than USD who value their time higher (compared to how much they earn).

In other words, EUR earners value an hour of their time at 1.8x more than an hour of paid work, NIS-earners 1.75x more, and GBP earners 1.57x more. US earners at the second last spot, with 1.48x. The last ones are INR earners, valuing an hour of their time 1.11x higher than an hour of paid work.

In short:

• European earners work less than North Americans, and value their time way more.
• Israeli earners work longer hours than North Americans, and value their time way more.

Based on the last two points, it looks like the amount of time you work, doesn’t seem to impact how you value you time.

This is surprising (at least for me). I’d expect that the more you work, and the less free time you have, the higher the value of your time would be to you. But, the relationship could be reverse: because you don’t value your time highly, you end up working more (maybe you have nothing else to do?). Or, you value your time very highly and spend it on work.

Based on the information in this data set, there seems to be no clear relationship between average time spent working per year and the value of time.

# 🥘 Takeaways

In general, it seems that earning more per hour doesn’t lead to valuing one’s time more. USD earners earn the most—in total and per hour—, but they don’t value their time as much as, for example, those who earn in euro or British pounds.

The length of time you spend working also doesn’t seem to impact how much you value your time. The direction of this relationship is unclear.

So, don’t go judging your 4-hour work week friends as lazy, perhaps they just value their time very highly. But, even if they earned a lot per extra hour of work, it wouldn’t necessarily lead them to value their time more.

My personal takeaway: consider getting an employer who pay is in euros — you will earn almost like the North Americans, but will work less. (And get more free time to write about datasets for Medium! A delight for everyone who read until here.)

There will be at least two more article in this time series (see what I did there?), so keep your eyes on what I publish in the coming weeks!

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Written by

## Marta

📈 Aspiring data scientist. Rationality fan. EA. Condensing a stream of thoughts into #100words a day. Need an accountability coach? 🔥 bit.ly/2NTP6eB

## The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +789K followers.

Written by

## Marta

📈 Aspiring data scientist. Rationality fan. EA. Condensing a stream of thoughts into #100words a day. Need an accountability coach? 🔥 bit.ly/2NTP6eB

## The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +789K followers.

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