Baselining my life (part 1, money)

Introduction

I began to seriously contemplate the idea of going freelance back in May, and realised I wasn’t very prepared. For one thing, I didn’t really know how much money I spent each month or what I spent it on. Nor did I have a good handle on how I spent my time. I wanted to see how I was behaving financially, so as to know how much I’d need to earn as a freelancer to maintain my lifestyle. I also wanted to know what made up this lifestyle to see how much time I’d have available for doing this freelance thing. I did go freelance at the end of June and carried on measuring things for a while so I’d have enough data for a meaningful result.

On the 6th June, I started keeping a record of my spending; on the 1st July, I began to keep a diary of what I was doing all day. At the end of August, I stopped measuring. This means I have four months of records of how I’ve spent money and three of how I’ve spent time.

Having this information is very useful, I think, so I’ve published all the data and analysis spreadsheets for you to play with. If you wanted to do something similar, they might be helpful. (The data are all published as Google Spreadsheets – see the links at the bottom of this post.)

Of course, it would be remiss of me, as a one-time scientist, if I were to look at data and deduce hypotheses after the fact. So, without prejudicing my judgement too much by figuring out a collection of perhaps-useful things in advance, I’ve written down some questions that I want answering and hypotheses to test for each.

Money

  1. How much money do I spend in a month?
    • Hypothesis: I think I spend £2400 a month
  2. What am I spending most of my money on?
    • I think the majority is being spent on rent, dining and boozing
  3. How much do I spend on booze?
    • I think I spend £400 a month on booze

Time

  1. How much time do I spend sleeping?
    • I think I spend 1/3 of my time sleeping
  2. How much time do I have free in a week after I’ve done all the habitual activities?
    • I estimated I have 31 hours a week to use (estimate recorded in Habits)
  3. What is the most expensive activity per time spent on it?
    • I think it is boozing (although dining will be a close second)

This post is going to cover the questions about spending money.

Constraints

  1. The Hawthorne effect – will I change my behaviour by measuring it?
  2. Not splitting money and time spending when on holiday – it just goes down as ‘holiday’

Measuring money

Answering the questions

How much money do I spend in a month?

Hypothesis: I think I spend £2400 a month
The data shows: Between May and August 2009, I spent an average of £2492.65 each month

What am I spending most of my money on?

Hypothesis: I think the majority is being spent on rent, dining and boozing
The data shows: rent 21.7%, holiday 20.6%, dining 10%, boozing 7.7%

How much do I spend on booze?

Hypothesis: I think I spend £100 a week or £400 a month on booze
The data shows: Between May and August 2009, I spent an average of £191.20 on booze each month

The data illuminates

The images below are generated from the summary spreadsheet, which contains live graphs you can play with – clicking on pie segments, for example, reveals the percentage and value attached to each.

Methods

Collecting data – iPod Touch & Notes

Online banking is a boon to the would-be data collector, as you can generally download a transaction history in csv format. Unfortunately, plenty of entries for “cash” creates a big unknown in the results. I decided to keep a record of when I spent cash during the day, what I spent it on and how much I spent. I didn’t start monitoring expenditure until the 6th May, so approximately £140 cash is unaccounted for over the four month period (I haven’t been worried sufficiently to alter any calculations to take this into account, as my monthly spending has a standard deviation of more than this).

I’d done something like this before, when I was having a crisis about where all my money was going shortly after I started working for BT in 2005. Back then, I used a pencil and notepad, and the prospect of copying all the records into a computer was so daunting that I never did. This time, I had the benefit of an iPod Touch (thanks Dad) and its “Notes” application. You can mail yourself a note, which I did every month or so.

Dealing with the data after it fell into my inbox was a matter of some search-and-replace in a text editor, to make the fields tab-separated so that they could be copy-and-pasted into Google Spreadsheets.

Analysing data – Google Spreadsheet

The first thing to do was enhance the data by tagging each expense with a chunky category like “rent” or “boozing”.

Using Google Spreadsheets to analyse an amount of information is a task with a fairly steep learning curve, since the examples for using the more complicated functions are not always that enlightening (I imagine if you are already an Excel functions master, it won’t be so hard). Nevertheless, the Google Docs forum is replete with the writings of two characters in particular – Otávio Alves Ribeiro, the Spreadsheet Ninja from Brazil, and “ahab“, the mysterious Google Docs Guru.

The performance of a Google Spreadsheet is generally quite good, although there is often slowdown in processing. I found, mainly through trial and error, that a good way to understand what is going on and get decent performance out of Google Spreadsheets is to separate worksheets between data collection and analysis; then perform any inter-spreadsheet aggregation by importing the plain data from each spreadsheet into a single worksheet and make all your calculations based on that.

Further work

I ended up with just over 20 different tags; if I were to do this again, I would split the ‘holiday’ tag into its constituent parts and I would split “dining” into “breakfast”, “lunch” and “dinner”.

It will be interesting to compare my monthly spend (which is the simplest data to measure) to the relatively steady pattern I have discovered over these four months to see how much effect measuring my spending had on dampening it; indeed, I could measure the historical monthly spends and perhaps see an effect that way.

Data

All the data for this experiment is published on various Google Spreadsheets. See the links below.

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10 Comments

  1. Posted September 10, 2009 at 5:54 pm | Permalink

    none of your spreadsheets are published…

  2. Posted September 11, 2009 at 11:09 am | Permalink

    Very interesting J. I’m tempted to follow your lead.

    Questions for you:
    1. Do you save anything? (I appreciate income is not on this)
    2. How much hassle was it to do?
    3. Do you think you changed your behaviour by recording it? (ala the Hawthorne effect)
    4. Were there any surprises? Are you going to adjust your spending on anything?

    Nick

    p.s. I’ll share the differences in my spending with you the next time I see you.

    • Posted September 11, 2009 at 11:37 am | Permalink

      Hey!

      1. Nope. I’ve been burning through my savings in the first two months to make sure there is a bit of a buffer in the company account.
      2. The cash tracking was not very much hassle. The hard bit was figuring out how to arrange and analyse the data once I had it.
      3. I think that I was less likely to make small cash transactions, because I knew I’d have to write it down. It at least made me think twice. I found with the time recording that there was a much stronger effect – I had to note down every time I changed context, so I simply stayed doing the same thing for longer.
      4. I thought I spent more on booze and a lot less on holidays. The data is slightly unrepresentative of a typical month because it was over the summer, when holidays tend to feature more heavily. The other thing that surprised me was how consistent my spending on most things was from month to month – the area chart shows this quite well.

      I’d be interested to know how things pan out if you decide to do something similar!

  3. Posted September 14, 2009 at 7:02 pm | Permalink

    Keeping track of ones personal spending is very difficult. However a company like TESCO capture this data using its club card.

    Wouldn’t it be nice if you had a ‘not-clubcard’ on which small businesses – grocers, butchers, bars – could load your receipt details and metadata about items bought. You’d then take it home and feed it into your spreadsheat and reconcile it with your bank, taking time to enjoy the double entry accounting system.

    There would be benefits to small businesses in such a scheme, many of them the same as those which the like of TESCO achieve; prediction of demand, opportunities for cross-marketing promotions and brand loyalty.

    Benefits for the consumer could be realised through mashing the data with other data. For example, total food and drink bought with miles that food has traveled, units of booze per month or total calories purchased per month.

  4. Posted October 20, 2009 at 12:52 pm | Permalink

    What a great experiment. A lot of this (if not all) should be much more automated by online banking sites.

    Ever tried using Wesabe? or Mint?

    Also how much easier would things have been if you had auto OFX/XML feeds from accounts? Would you have done anything differently if these were available?

  5. Posted March 10, 2010 at 8:59 am | Permalink

    This is the reason I love jaybyjayfresh.com. Incredible psots.


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  1. […] going (I’ve yet to write this up, although the sister experiment on what I spend money on is here). In advance, I’d predicted what I spend time on – things like sleeping, eating, […]