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True experiments and quasi-experiments are conducted and the results are evaluated later. Natural experiments, by contrast, are going on around us all the time. They are not conducted by researchers at all—they are simply evaluated.

Here are four examples of common natural experiments: (1) Some people choose to migrate from villages to cities; others stay put. (2) Some villages in a region are provided with electricity, some are not. (3) Some middle-class Chicano students go to college, some do not. (4) Some cultures practice female infanticide, some do not.

Each of these situations is a natural experiment that tests something about human behavior and thought. The trick is to ask: ‘‘What hypothesis is being tested by what’s going on here?’’

To evaluate natural experiments—that is, to figure out what hypothesis is being tested—you need to be alert to the possibilities and collect the right data. There’s a really important natural experiment going in an area of Mexico where I’ve worked over the years. A major irrigation system has been installed over the last 50 years in parts of the Mezquital, a high desert valley. Some of the villages affected by the irrigation system are populated entirely by Nahnu (Otoml) Indians; other villages are entirely mestizo (as the majority population of Mexico is called).

Some of the Indian villages in the area are too high up the valley slope for the irrigation system to reach. I could not have decided to run this multimillion-dollar system through certain villages and bypass others, but the instant the decision was made by others, a natural experiment on the effects of a particular intervention was set in motion. There is a treatment (irrigation), there are treatment groups (villages full of people who get the irrigation), and there are control groups (villages full of people who are left out).

BOX 4.5


In classical economic theory, it is assumed that Homo economicus is programmed to maximize gain in any bargaining situation. In 1982, however, economists in Germany found evidence that challenged this assumption (Guth et al. 1982). In what the researchers called the ultimatum game, there are two players who never see each other or find out the other's identity. Player 1 gets a sum of money, x, and offers some fraction of it, x— p, to player 2 through an intermediary. If player 2 accepts, the money is split according to the terms of player 1. If player 2 rejects the offer, then neither player gets anything. Economic theory predicts that player 1 should offer next to nothing and that player 2 should accept anything player 1 offers. As Thaler put it, ''the data are inconsistent with both of these predictions'' (1988:197). For player 2, it costs nothing to reject very small offers—and to punish player 1—but as the fraction of the offer goes up, player 2 has to weigh the joy of teaching the other player a lesson with the loss of real money. If you're player 1, the trick is to figure out how much to offer so that you don't wind up with nothing but you still get what you can.

By the late 1990s, there were hundreds of studies using economics games in developed countries. Then, Joseph Henrich and colleagues (2005) ran the ultimatum game in 15 small-scale societies around the world—from foragers to farmers—as well as with American university students. ''The selfishness axiom,'' they conclude ''was violated in some way in every society we studied'' (p. 803). And the violations weren't random. The more integrated people were into a market economy, the more prosocial behavior they exhibited in the games (p. 795) (Further Reading: economic games in anthropology).

Unfortunately, I can’t evaluate the experiment because I simply failed to see the possibilities early enough. Finkler (1974) saw the possibilities; her ethnographic study of the effects of irrigation on an Indian village in the same area shows that the intervention is having profound effects. But neither she nor I measured (pretested) things like average village wealth, average personal wealth, migration rates, alcoholism, and so on that I believe have been affected by the coming of irrigation.

Had anyone done so—if we had baseline data—we would be in a better position to ask: ‘‘What hypotheses about human behavior are being tested by this experiment?” I can’t reconstruct variables from 20 or 30 years ago. The logical power of the experimental model for establishing cause and effect between the intervention and the dependent variables is destroyed.

Some natural experiments, though, produce terrific data all by themselves for evaluation. In 1955, the governor of Connecticut ordered strict enforcement of speeding laws in the state. Anyone caught speeding had his or her driver’s license suspended for at least 30 days. Traffic deaths fell from 324 in 1955 to 284 in 1956. A lot of people had been inconvenienced with speeding tickets and suspension of driving privileges, but 40 lives had been saved.

Did the crackdown cause the decline in traffic deaths? Campbell and Ross (1968) used the available data to find out. They plotted the traffic deaths from 1951 to 1959 in Con?necticut, Massachusetts, New York, New Jersey, and Rhode Island. Four of the five states showed an increase in highway deaths in 1955, and all five states showed a decline in traffic deaths the following year, 1956. If that were all you knew, you couldn’t be sure about the cause of the decline. However, traffic deaths continued to decline steadily in Connecticut for the next 3 years (1957, 1958, 1959). In Rhode Island and Massachusetts, they went up; in New Jersey, they went down a bit and then up again; and in New York, they remained about the same.

Connecticut was the only state that showed a consistent reduction in highway deaths for 4 years after the stiff penalties were introduced. Campbell and Ross treated these data as a series of natural experiments, and the results were convincing: Stiff penalties for speeders saves lives.

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