Skip to content

Econometric methodology for human mating

econometric-methodology2 I recently helped one of my single male graduate students in his search for a spouse.

First, I suggested he conduct a randomized controlled trial of potential mates to identify the one with the best benefit/cost ratio. Unfortunately, all the women randomly selected for the study refused assignment to either the treatment or control groups, using language that does not usually enter academic discourse.

With the “gold standard” methods unavailable, I next recommended an econometric regression approach. He looked for data on a large sample of married women on various inputs (intelligence, beauty, education, family background, did they take a bath every day), as well as on output: marital happiness. Then he ran an econometric regression of output on inputs. Finally, he gathered data on available single women on all the characteristics in the econometric study. He made an out-of-sample prediction of predicted marital happiness. He visited the lucky woman who had the best predicted value in the entire singles sample, explained to her how he calculated her nuptial fitness, and suggested they get married. She called the police.

After I bailed him out of jail, he seemed much more reluctant than before to follow my best practice techniques to find out “who works” in the marriage market. Much later, I heard that he had gotten married. Reluctantly agreeing to talk to me, he described an unusual methodology. He had met various women relying on pure chance, used unconscious instincts to identify one woman as a promising mate, she reciprocated this gut feeling, and without any further rigorous testing they got married.

OK, all of us would admit love is not a science. But there are many other areas where we don’t follow rational decision-making models, and instead skip right to a decision for reasons that we cannot articulate. A great book on this is by Gerd Gigerenzer, Gut Feelings: The Intelligence of the Unconscious. There is also the old idea that not all useful knowledge can be explicitly written down, but some of it is “tacit knowledge” (see any writings by Michael Polanyi).

Is the aid world more like love or science? Probably somewhere in between. Obviously, there is a BIG role for rigorous research to evaluate aid interventions. Yet going from research to implementation must also involve a lot of gut instincts and tacit knowledge. I know experienced aid workers who say that they can tell right away from a site visit whether the project is working or not.

I don’t know if this is true, but certainly implementation involves non-quantifiable factors like people who have complicated motivations and interactions. A manager of an aid project must figure out how to get these people to do what is necessary to get the desired results. The manager (who also has complicated motivations) must adjust when the original blueprint runs into unexpected problems, which again relies more on acquired tacit knowledge than on science. (How to keep the bed net project going when the nets were first impounded and delayed at customs, the truck driver transporting the nets got drunk and didn’t make the trip, the clinic workers are off at a funeral for one of their coworkers, the foreign volunteer is too busy writing a blog and smoking pot, and the local village head is insulted that he was not consulted on the bed net distribution.) Certainly something similar is true also in running a private business or starting a new one – there is no owner’s manual for entrepreneurship.

So for donors and managers of aid funds, is finding the right project to fund more like econometrics or is it more like falling in love? How about a bit of both?

This entry was posted in Aid policies and approaches, Data and statistics. Bookmark the permalink. Follow any comments here with the RSS feed for this post. Both comments and trackbacks are currently closed.

13 Comments

  1. you jest, of course. But The last chapter of Gary Becker’s Accounting for Taste is an equation for marriage. Economists who can only think like economists are horrific things.

    Posted October 26, 2009 at 6:30 am | Permalink
  2. Jeremy P wrote:

    Honestly, what an abysmal post this is.

    Posted October 26, 2009 at 7:55 am | Permalink
  3. Aid Watch wrote:

    Jeremy P, thanks for your feedback, can you be a tad more specific? Thanks, Aid Watch

    Posted October 26, 2009 at 8:55 am | Permalink
  4. Holli wrote:

    Love the new look of the blog and the great analogies in this post!

    I especially loved the description of the players in the bed net distribution channel…

    Great blog – keep up the good work. You are linked over at the Ramblings – so I can share your great stories with others!

    Posted October 26, 2009 at 10:13 am | Permalink
  5. Dennis wrote:

    Bill,

    As much as we hate to admit it, even experts’ decisions are often based on infatuation. In my career, I have found this to be true at official agencies where I have worked (World Bank, ADB, USAID), as well as at major foundations with whom I have interacted. We can either accept this and work with it, or rail against it and be frustrated. As much as I am in favor of more analysis and evaluation – which is critical – the data alone will never drive our decisions. The reasons for this are many fold, and include aptitude, expertise, attitude, upbringing, life experience (and probably hormone levels:)). Dennis

    PS: A description and discussion of my own infatuations over the years is here.

    Posted October 26, 2009 at 10:47 am | Permalink
  6. George wrote:

    Agree with Jeremy P, abysmal post. Bill, your friend did get married but you say nothing about a performance framework for the marriage – an abysmal failure as without this how will your friend be able to know whether the marriage is worth his ongoing investment?

    He needs a performance framework with quantitative, measurable targets (not vague qualitative statements like “…. still in love….”). Some combination of the following – number of arguments per month (specify decibel threshold), average smile size, % nights uninterrupted sleep, average duration intercourse etc. – ought to do it, and remember, if its not measurable (in principle – let`s not get bogged down in the practical availability and reliability of data just yet).

    A thorough stakeholder consultation would be necessary to ensure he´s got the right indicators – best mates, close relatives and (perhaps) his intended spouse would seem obvious choices for consultation, although perhaps to be fair to Anyone who might feel that had the right to comment, he could just advertise for those interested to get in touch in some obscure journal.

    In this way he would know if the marriage was a success and would be able to withdraw his investment soon as it became objectively apparent that his investment was no longer paying off.

    But no…. 30 grinding years of immeasurabley sub-standard marital relations later, it would all be Bill´s fault!

    Abysmal.

    Posted October 26, 2009 at 11:45 am | Permalink
  7. loyal reader wrote:

    great post. keep them coming just like this one

    also, no mention of incentive systems to reward good marital performance? tsk tsk.

    Posted October 26, 2009 at 12:04 pm | Permalink
  8. caveat bettor wrote:

    How do you square this with Duggan’s ‘strategic intuition’ or coup d’oeil? I mean, didn’t Napoleon fight from the gut?

    Posted October 26, 2009 at 12:24 pm | Permalink
  9. Michael Clemens wrote:

    This is a perfect example of how to communicate a complex idea concisely via an imperfect yet apt analogy.

    Posted October 26, 2009 at 12:34 pm | Permalink
  10. Jeff Toohig wrote:

    Ha! Fantastic. And some great comments in response, especially George.

    Not everything measurable is important and not everything important is measurable.

    Posted October 26, 2009 at 2:00 pm | Permalink
  11. Jim wrote:

    Great post. I think the key to a good relationship is the opposite of the key to good economics – forget about the opportunity cost.

    Posted October 27, 2009 at 10:59 am | Permalink
  12. D. Watson wrote:

    Too Much Fun with analogies:

    1 – He ignored the self-selection bias. It’s only about qualities that make the average guy happy if he thinks he is the average guy. He needed to find a bunch of guys very similar to himself and examine the qualities that made a difference FROM AMONG the population of women willing to marry guys like him. If he then approached a women who was not in that sample, no wonder he was rejected.

    2 – He ignored endogeneity. Marriage is a matching game with (at least) 2 endogenous variables. Instead of trying to convince her that research showed she would make him happy, he needed to present research that demonstrated he would make her happy, and that’s the other half of the regression: male qualities on marital happiness. No wonder she rejected him: his regressions didn’t answer her question!

    Personally, I took more of a Bayesian approach. First, by trying to become a friend, I identified if she was in the group of people who would marry someone like me. Each interaction gave me more information about the error term. After any failed relationship, I had a new variable or two to add to my equations so that the error term got closer and closer to being white noise and the standard errors on the coefficients got smaller as I understood the ‘relationships’ between relationship variables better. An alternative way of putting that is that I learned more and better policies over time that I could enact in the particular situations I was likely to face.

    In the end, I’ve married someone who has all the good qualities in the best people I dated and avoids all of the relationship ending problems, and I’ve learned how to make such a person happy so I can keep her.

    Posted October 27, 2009 at 3:03 pm | Permalink
  13. florian wrote:

    Maybe one should look at the results to determine the more appropriate approach (brain vs. guts). In Germany e.g. the divorce rate is more than 50% – pls compare with the rate of failed aid projects. For more indepth analysis it might be helpfull to look at mid- and long-term results: failure within the first year, after 3, after 7 years of marriages/projects.
    Randomly collected field data from Sri Lanka suggests that the scientific, or rather economic, approach (i.e. arranged marriages) yields more sustainable results than the feelings-based (i.e. love marriages).
    If this is true for marriages maybe it is worth considering for aid projects as well…

    Posted October 28, 2009 at 4:33 am | Permalink

5 Trackbacks

  1. By Les liens du matin (61) « Rationalité Limitée on October 27, 2009 at 3:04 am

    [...] liens du matin (61) * “Econometric methodology for human mating” – William [...]

  2. By Some thoughts on MDGs 2.0 « Aid Thoughts on October 27, 2009 at 6:43 am

    [...] hope the analogy is clear -  we development bloggers are not known for our wonderful analogies (sorry Bill). One of the main criticisms of the Millennium Development Goals was that, as global, [...]

  3. By uberVU - social comments on October 27, 2009 at 3:09 pm

    Social comments and analytics for this post…

    This post was mentioned on Twitter by MarkThoma: Econometric methodology for human mating – Aid Watch http://icio.us/kdzqju...

  4. [...] Talking of aid sceptics, Bill Easterly asks if aid is more like science or falling in love [...]

  5. [...] académico que esté a la última. Sin embargo, la realidad está mucho más a ras de suelo. En este divertido artículo, el  cínico pero  agudo Bill Easterly habla de lo que realmente pasa en un proyecto de [...]