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Posts Tagged ‘Tim Harford’

As you may have read earlier, Ermintrude and I attended Tim Harford’s talk at 1 Alfred Place last Thursday.  Following on from Ermintrude’s post about the first half of Tim Harford’s talk where he engaged us with the economics of happiness; I thought I’d fill you in on the second half of the talk which was even juicier… involving the economics of dating!

It may seem a bit strange upon first consideration that there have been studies carried out on dating, as economists ideally need large amounts of data in order to come up with any substantial conclusions.  However, it turns out that the invention of speed dating has saved economists from turning up on their mates’ dates dressed in lab coats and toting clipboards.  Vast amounts of data have been generated by speed dating events in the following way:

1)      Potential participants sign up for the events, and in the process provide information about themselves in the form of gender, education, height, and earnings, etc.

2)      Participants turn up to the event, and spend five minutes (or some other such short amount of time) chatting with each of the participants of the opposite sex, in short, having a ‘speed date’.

3)      At the end of a speed dating event, participants turn in their dating cards, where they tick either one of two boxes: ‘yes’ or ‘no’, for each person they met.  If they ticked a ‘yes’ for someone who has also ticked a ‘yes’ for them, they will receive each other’s contact details, and vice versa.  A ‘no’ from either person means that neither of them would receive contact details.

This process, theoretically, keeps our daters honest.  There may be a compulsion to tick ‘yes’ to every box to check who else has said ‘yes’ to them, but this would mean their contact details would be received by people they may not have liked.  On the other hand, if participants are shy and tick ‘no’ for someone they did like, they’d never get the chance to see them again.  Even if they ticked ‘yes’ and the other person ticked ‘no’, the other person need never know that they were chosen by our ‘shy’ participant.

So what did economists find out?  Unsurprisingly and unfortunately, stereotypes rule.  Both men and women prefer non-smokers with higher educations.  Women prefer taller men with higher incomes, while men prefer slimmer women. 

The results also show that no matter the calibre of men available at the event, women by and large ticked about 10% of their ‘yes’ boxes.  For example, for every 20 men, women will always tick ‘yes’ to an average of 2 men, whether the group of men are shorter and less educated with low incomes, or the group consists of taller, higher income men with IQs over 130.  Men ticked a higher percentage of ‘yes’ boxes, however this percentage also did not change across different groups of women.

Tim referred to this last phenomenon as a victory for the economists over the romantics – with the romantic ideal of ‘the one’ or ‘the select few’, and the economist’s view that the dating scene is, in short, a market, with consumers selecting the best of what is currently available at any point in time.

I took time out to test this theory with the other heifers in the pasture, at least one of whom has participated in speed dating events in the past.  We came up with a few counter-points:

1)      The way that speed dating companies set up the particular rules of the events can affect the data. For instance, one heifer pointed out that on her event, in order to find out how many men had ticked ‘yes’ to her, she had to tick at least one ‘yes’ herself, and this curiosity drove her to tick that box for the least offensive man.  It also helped that instead of full contact details, our heifer only had to offer up her e-mail address; emails can easily be ignored and an email address is changeable as a last resort.

2)      Participants who turn up to events where the participants of the opposite sex are less/more desirable according to the usual statistics, may themselves be less/more desirable, and their standards higher/lower accordingly.  For instance, the type of people that would turn up to a speed dating event hosted by a university would be different from the type of people that would turn up to a speed dating event hosted by a pub.

3)      The dataset is necessarily biased, as it is data based on a subset of the entire dating population, that is, the subset that would go on a speed dating event.  Presumably the more romantically minded would not attend such an event, preferring instead to leave their chances of meeting their love of their life to serendipity.

I, Betsy, admit that although I would like to think of myself as an economist, I am of the more romantically minded (some people would call me hopeless, really).  However, I do like the concept of using economics to make decisions in everyday life, in much the way that Tim Harford utilises economics in his FT column, “Dear Economist”.  Therefore I propose a tongue in cheek question to all other romantic economists out there:

Would open relationships produce the most efficient pairings?  If you are locked into a relationship, you are essentially monopolised, unable to try other ‘products’ on the ‘market’, which invariably leads to an inefficient market.  What do you think?

For more information, read Tim Harford’s blog post on the economics of dating here: http://timharford.com/2007/11/business-life-the-economics-of-dating/

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Yesterday evening Betsy and I had the pleasure of hearing a talk by Tim Harford, better known as the undercover economist.  During which we covered the banking crisis, the economics of dating and happiness.  It is this last point that I wish to share with you.  There is a huge amount of literature relating to ‘happiness’ and ‘being happy’ most of which I am going to bypass as Tim Harford or as we like to call him Hooford has gone through the process of picking out some of the most interesting bits and covering them in his talk.  During a study conducted in the 1990s individuals were asked about their happiness.  The results found those who rated themselves most happy were married, had no children, were women, had a high level of education and were employed however, it was stressed that these links were not causal.  Anyway it got me to thinking am I the happiest cow in the pasture?

So being the curious type I decided to check whether this was indeed the case by comparing my answer to the question, “how happy with life are you on a scale of 1-7 with 7 being very happy?” with the answer from the other cows in the pasture.  Being economists however they bombarded me with a series of further questions relating to the exact definition of happiness, what the counterfactual or baseline was and accusations that as I was asking on a Friday and we’d be able to roam free in the pasture over the weekend everyone would be happy.

I decided to press on and gave the following definitions:

Happy 7 = like getting up in the morning and looking forward to every day

6= like getting up in the morning and looking forward to some days

5 = like getting up in the morning and looking forward to the occasional day

4= no feeling either way about getting up and getting through each day

3= occasionally do not want to get out of bed and don’t look forward to the occasional day

2=often do not want to get out of bed and don’t look forward to most days

1=never want to get out of bed and don’t look forward to every day

So did the model work?

The predicted outcome in terms of happiness rankings in the pasture would be

Ermintrude (happiest),

Daisy, Elsea, Buttercup,

Aberdeen Angus, Limu,

And finally, Hickory Smoked Prime Rib (least happy).

The actual rankings were

Elsea (happiest),

Buttercup,

Aberdeen Angus,

Hickory Smoked Prime Rib,

Ermintrude,

Limu,

Daisy (least happy).

So how why did the model’s predictions fall foul of the field.  Well it seems that all cows in the field were extremely happy with mere decimal points separating some contenders.  In addition, some cows gave error bars relating to their rankings that were not taken into account during this rough and ready process.  However, the main issues can be split into two camps one to do with framing the question and the other to do with unobserved factors.  My definition of the ‘happiness’ question and the scale used were both open to interpretation, with the happiness scale potentially differing between all of us.  Unobserved factors – such as my long commute home and subsequent lack of sleep following last night’s talk may have impacted how ‘happy’ I felt this morning.  Likewise, cows concluding projects, not getting sleep when their calves wake them up, and a general dislike of mornings all contribute to this variable but were not accounted for in the model.

To find out more about the economics of happiness and Tim Harford’s talk please check out his blog http://timharford.com/2009/09/the-economists-guide-to-happiness/

– Ermintrude

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