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Dependent and Independent Variables

Beginning in the 1840s, breakthroughs in sociology and anthropology produced insights into the impact of economic and political forces on demography. One practical result of all this work was life insurance. The way life insurance works is that you bet the company that you’ll die within 365 days. You answer a few questions (How old are you? Do you smoke? What do you do for a living? Do you fly a small plane?), and the company sets the odds—say, your $235 against the company’s promise to pay your heirs $100,000 if you win the bet and die within 365 days. But if you lose the bet and stay alive, they keep your $235, and next year you go through all this again, except that now the odds are raised against you to, say, your $300 against the company's promise to pay your heirs a lot of money.

For insurance companies to turn a profit, they have to win more bets than they lose. They can make mistakes at the individual level, but in the aggregate (that is, averaging over all people) they have to predict longevity from things they can measure.

Longevity, then, is the dependent variable, because it depends on sex, education, occupation, etc. These latter are called independent variables because they are logically prior to, and therefore independent of, the dependent variable of longevity. How long you live doesn’t have any effect on your sex. In our earlier example, blood pressure was the dependent variable. There is no way skin color depends on a person's blood pressure.

It's not always easy to tell whether a variable is independent or dependent. Does high female infant mortality among Amazonian tribal people depend on high levels of warfare, or is it the other way around? Does high income depend on having a lot of land, or vice versa? Do inner-city adolescent girls get pregnant because they are poor, or . . .? Does the need for litigation stimulate the production of attorneys, or ... ?

Failure to understand which of two variables depends on the other is the source of endless shenanigans. One of my teachers, Oscar Lewis (1961, 1965), described what he called a ‘‘culture of poverty’’ among slum dwellers in cities around the world. People who live in a culture of poverty, said Lewis, are not very future oriented. This plays out, he said, in their shopping for food every day and in never buying large economy sizes of anything. Lewis’s point was that truly poor people can’t invest in soap futures by buying large boxes of it. He saw a low level of expressed orientation toward the future, then, as the dependent variable and poverty as the independent variable.

Many people interpreted Lewis’s work as meaning exactly the opposite: that poverty is caused by a low level of future orientation. According to this topsy-turvy, victim-blaming reasoning, if poor people everywhere would just learn to save their money and invest in the future, then they could break the poverty cycle. Such reasoning may serve to create pointless programs to teach poor people how to save money they don’t have, but it doesn’t do much else.

In rural West Virginia, for example, there is a lot of teen pregnancy and many adolescents drop out of high school. Since the 1960s, according to Bickel et al. (1997), state policymakers in West Virginia have blamed these behaviors on the culture of poverty. The behaviors that state policymakers want so much to change, however, are caused by the continuing deterioration of economic and social conditions in rural communities. No amount of‘‘educating’’ poor people about their bad habits will change the material circumstances that cause the so-called culture of poverty (box 2.2).

 
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