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Poverty isn’t disarmingly simple

August 12, 2010

In a New Republic piece, John McWhorter wrote the following, which was later excerpted on the Daily Dish, thereby becoming a part of the Internet’s collective consciousness:

One of the most sobering observations made by Wax comes in the form of a disarmingly simple calculus presented first by Isabel Sawhill and Christopher Jencks. If you finish high school and keep a job without having children before marriage, you will almost certainly not be poor. Period. I have repeatedly felt the air go out of the room upon putting this to black audiences. No one of any political stripe can deny it. It is human truth on view. In 2004, the poverty rate among blacks who followed that formula was less than 6 percent, as opposed to the overall rate of 24.7 percent. Even after hearing the earnest musings about employers who are less interested in people with names like Tomika, no one can gainsay the simple truth of that advice. Crucially, neither bigotry nor even structural racism can explain why an individual does not live up to it.

If I could advise all of the teenagers in our country, I would advise them to stay in school and wait to have children until they were employed. But despite the “disarmingly simple calculus” above, I doubt my advice would be a panacea for national poverty rates. The true calculus is much less simple than the one McWhorter describes.

The technical part wherein I try to explain the importance of a balanced data set

The statistical evidence cited seems to be a simple cross-tabulation, and not a particularly interesting one: how surprised are any of us that blacks who complete high school and keep a job are much less likely to be poor? Perhaps Sawhill and Jencks ran a regression analysis that found that, controlling for other variables, completing high school and delaying parenthood are negatively associated with poverty. I have no reason to doubt this: I completely believe that married, diploma-holding African-American parents are much less likely to live in poverty.

Unfortunately, regression is unable to test for causal effects wherever there might be “heterogeneous treatment effects.” Basically, people who choose to complete high school in part choose to do so because they believe a high school degree will benefit them. People who choose not to finish high school may in part choose to do so because they believe a high school degree will not benefit them as much. In a lot of cases, these beliefs might even be accurate.

Imagine that we have a sample of 100 adults. Half completed high school and earn $100/week. Half dropped out of high school and now earn $50/week. Bivariate regression would tell us that the a high school degree is associated with an increase in your earnings of $50/week, and so we might want to assume that if we could encourage all of the drop-outs to get their GEDs, then they too would earn $100/week. But this only makes sense if we make the – strong and often unwarranted – assumption that a high school degree has the same effect for everybody.

Here is a better idea. Find two-hundred teenagers. Next, randomly administer some “intervention” or “treatment” by which we insure that 100 kids graduated and 100 kids did not. 10 years later, we would check in on these 200 adults and compare the average earnings of the graduate group to the average earnings of the didn’t-graduate group. Because we know that teenagers didn’t choose their treatment based on their beliefs about the effects of the treatment, we could now safely say, “The effect of obtaining a high school diploma for such-and-such a group of teenagers is $X.”

Unfortunately, experiments like that described above are often prohibitively expensive, impossible, or just plain immoral. The next best alternative is to create a “balanced data set” in which we match pairs of individuals based on their likelihood of receiving the treatment (in this case, graduating high school). For example, we would want to find two adults whom we determine to have a 75% probability of having finished high school, but one of whom finished and one of whom didn’t. In other words, “75% of people that look like these two finish high school, but one did and one didn’t.” We would then compare the differences in income of the graduates and non-graduates within these matched pairs. Only by comparing these differences can we hope to estimate the causal effects of a treatment where “selection into treatment” bias may exist.

In short, without “balancing” our data whenever selection bias exists as described above, we can only speak about associations. If we do balance our data, we can then speak about casual effects.

Why this matters

McWhorter and Wax have identified several factors that are strongly and starkly correlated with living in poverty. The implication in McWhorter’s piece is that these behaviors cause one to live in poverty. Furthermore, the behaviors that McWhorter have identified occur during adolescence, when we can imagine teens choosing whether or not to do the right thing. In short, McWhorter has expounded a causal logic that heavily implies something like, If you had only made him wear a condom, then your chances right now of being poor would be 6% instead of 24.7%.

By arguing that the most crucial junctures are those that happen during adolescence, McWhorter makes poverty seem like such a simple bullet to dodge. Just do the right thing, teenagers! But what if poverty is overdetermined by the same background factors that push certain segments of the teen population toward these behaviors? McWhorter’s story ignores the fact that by the time you are 13 you already have a long history: teenagers at high risk of pregnancy are not otherwise identical to teenagers at low risk. High risk teenagers may know that a diploma would benefit them less than their peers.

None of this is to deny that we should do everything we can to encourage teenagers to delay pregnancy and graduate high school. These choices undoubtedly improve life chances on the margins. But the oversized effects that McWhorter celebrates are just not supported by the statistics he cites. Poverty isn’t so simple, and making it appear so only encourage us to preach from the gospel of personal responsibility and then call it a day.

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