Chris Blattman recently blogged about the moral absurdity of running regressions where the dependent variable is “war deaths”.  While looking at death, illness, hunger, and poverty through the lens of statistics may seem rather reptilian, I think many researchers have emotional reactions to the data they work with.  For me, these connections hit hard and unexpectedly, often when I am tired and working late, and they come despite efforts to be dispassionate about the data I am looking at.

Survey editing is prime territory for emotional connections to data. When editing surveys, you see the story of an individual respondent in a way that you don’t when you are looking at columns of aggregated data .  Once, I was reading a survey where a respondent reported that a household member had experienced a headache.  I turned the page to the question on outcomes of health events.  The headache had resulted in death for that household member—despite the family spending an amount equal to roughly one-fourth of Ghana’s annual GDP per capita on health care for that individual.  The shock of the outcome hit me almost physically. Another respondent reported testing positive for HIV. Sitting alone in the Tamale office at night, I struggled to pull myself together, shoo the bugs out of my computer keyboard, and make my way home.

The “death” outcome became a dependent variable in regressions I later ran looking at determinants of health outcomes.  Luckily, there were very few events of death in my sample.  We also looked at a number of food insecurity events: individuals going to bed hungry, or not eating for an entire day, for example.  These were, unfortunately, common among our respondents.  I don’t deal well with feeling hungry myself, and for me, food insecurity statistics evoke desperately sad, human images: a man’s disappointment at foregoing his favorite fish; a young student trying to sleep before an exam while feeling the distracting ache of hunger; an elderly woman going without food for a day so her grandchildren can eat; a mother having to tell her thin children there is no food today.  

These emotional connections often seem like a distraction, something that prevents us from approaching our analysis logically and dispassionately.  In all honesty, part of my attraction to quantitative research tools might be to protect myself from these types of emotional connections to problems.  But it our ability to have these connections, even through layers of statistics, is tied to a very deep belief in the importance of what we are doing, and that counts for something.  Hey, at least I’m not working in finance.

When I am in Tamale, I often share my lunch with the office monkey, George. It seems to me that when I share my food with George, my eating habits improve.

There are two reasons for this. The first is simply related to quantity. I don't like to waste food, so normally I clean my plate even if I am no longer hungry.  When George is around, I give him my leftovers when I get full.

The second reason has to do with externalities. When I buy food for myself, I often get lazy and buy fried street food that is available nearby.  If I share food like this with George, I feel bad for feeding him rubbish.  I used to buy egg pies and give George the egg yolk, which I don't like anyway, before I decided that monkeys should probably watch their cholesterol too. As a result, if I know I will be sharing with George, I am more likely to travel farther to get healthy food I will feel good about sharing with the monkey.

I don't know a lot about the field of behavioral economics, but it seems to me that humans, as social animals, may be psychologically wired to internalize externalities through the feelings of guilt and warmfuzzies/self-righteousness.