Econometric methodology for human mating

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?