Happiness, children, and the difficulties of trying to answer Why-type questions

Wil Wilkinson points to an interesting article by Nicholas Eberstadt (and adds some comments of his own) on the topic of the high birth rates in the United States compared to Europe. Wilkinson attributes the difference to Americans’ higher average rates of reported happiness and, regarding government policy, cites Shelly Lundberg and Robert Pollak to suggest that birth rates could be raised via policies leading to lower unemployment for young adults. I know there have been some studies of the relation between local economic conditions and birthrates, but I can’t remember the findings. I seem to recall some interactions, with different patterns among different ethnic groups.

The business of unemployment and children is interesting, since from an abstract perspective I suppose that lower unemployment is a good thing, but so are lower birthrates (at least in the U.S., where the population is growing via in immigration anyway). And of course if people are unemployed, presumably they have more time to take care of the children. Maybe “unemployment” isn’t quite the right measure here.

To continue with the economic argument . . . Wilkinson writes, “I like the optimism explanation. It’s easy to see why folks would refrain from reproduction if they thought their kids had only a broiling, denuded planet full of wretched consumer-zombies living pointless lives in cookie-cutter McMansions and soulless big box strip malls to look forward to.” This isn’t quite right, I think: McMansions are good things to have–I think that pessimism is thinking you’ll live in a bad neighborhood, not that you’ll live in a McMansion.

What I really meant to say was . . .

Anyway, the real reason I brought this up was not to talk about happiness and birth rates (on which I’m no expert) but to discuss the challenges of the “why” sort of causal inference. It’s a basic mode of science (and of social science): we see stylized fact X (in this case, higher birth rates in the U.S. than in Europe) and then try to make various comparisons to figure out the causes of X.

But Rubin has taught us to look for the effects of causes, not the causes of effects. A similar problem arose in our Department of Health study where we were trying to understand the different rates of rodent infestation comparing whites, blacks, and hispanics in NYC. Even after controlling for some available information such as the neighborhood, the quality of the building, the floor of the apartment, etc., there were more rodents in the apartments of ethnic minorities. We’d like to “explain”–understand–this pattern, but this sort of reasoning doesn’t fit directly into the statistical framework of causal inference. One approach is to reframe things in terms of potential intervntions (as I’ve done above with the birthrate example by imagining policies that lower unemployment). But that doesn’t seem to completely get at Wilkinson’s question about happiness.

4 thoughts on “Happiness, children, and the difficulties of trying to answer Why-type questions

  1. I'm in favor of lower unemployment for young adults, and of a lower birth rate. I hope that both can be achieved.

    A recent New York Times Magazine article discussed suburbs (and implicitly McMansions, although that was not a focus) in the context of commute times, with the theme that people underestimate the effect of a long commute on their happiness and that most people would probably be happier with a shorter commute even though it would probably mean a smaller or otherwise less desirable house.

    I have a prejudice against McMansions: I associate the perceived need for a really big house with both misplaced priorities and with spiritual emptiness. So although they may not be intrinsically bad, I disagree with Andrew's pro-McMansion stance.

    As far as the rat study goes: I would think that a way to approach this problem — what are the additional factors, not included in your current models, that lead to more rats in the apartments of ethnic minorities — would be to look at it from the rat's point of view. Presumably there are things that rats need or want (access to food, water, shelter; lack of rat traps and baits; lack of dogs and other predators) that are more common in the minority households. Perhaps talking to a rat expert rather than a statistician (or in addition to a statistician) would help come up with a solution.

  2. Phil,

    I'm not pro-McMansion (although I see how my paragraph could have been read that way), I just think that they're things people aspire to. I was disagreeing with the quote that implied that people would be sad if they felt that all they could look forward to was a McMansion.

  3. Andrew, I like the effects of causes idea, I think, although I hadn't thought of it exactly that way. I deal in system dynamics, which is essentially feedback control theory applied to human and organizational systems. There we look for loops, so a "thing" is likely both a cause and an effect (or A causes B and B, perhaps through a longer chain, causes A). In such cases, it's the loop (the structure) that causes the overall behavior. How do you go about analyzing that?

    From my engineering days, I'd think about injecting low-level, broadband noise in the loop somewhere and measuring both the transfer function as a function of frequency from just after point A until just before point A (i.e., all the way around the loop) to understand the dynamics (the cause and effect) and the coherence function to see what part of the response was due to the injected noise and what was due to other noise (in other words, to see ranges of frequencies over which the calculated transfer function was bogus).

    The standard way to calculate that, AFAIK, is through FFTs (or perhaps through so-called swept analyzers (multiply the filtered signal by a swept sine wave and detect the amplitude of one of the product terms)), but engineers typically have much more data to work with (I'm sort of assuming that it's likely to be an oscillating signal, even if it has a very long period). It'd be great to have an approach that would work with one or two periods of an oscillation.

    I'm not really looking for answers (although I'll take any you offer) but mostly just noting that cause and effect gets a bit strange in feedback loops.

  4. I wonder if Phil might post the date of the issue that the NYT Magazine article on commute times from the suburbs appeared in.

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