Under the Influence by Robert H. Frank

Author:Robert H. Frank [Frank, Robert H.]
Language: eng
Format: epub
ISBN: 9780691193083
Publisher: PrincetonUP
Published: 2020-11-28T00:00:00+00:00

FIG. 9.5. Obesity and energy intake in the United States, 1961–2009. Adapted from CDC NHES and NHANES 1960–2008.

At month’s end, canvassers collected data on electricity use for the four groups of homes. And sure enough, residents who had been told of neighbors’ conservation efforts showed by far the biggest reductions in electricity use levels.24 Discussing this experiment with the New York Times, Cialdini echoed a principal theme of this book: “We think of ourselves as freestanding entities: ‘Oh, I’m independent of the influence of those around me. I’m an individual.’ In fact, we are swept by that information in ways we don’t recognize.”25

In an experiment inspired by Cialdini’s work, the economist Hunt Allcott designed a letter to electric utility customers living in comparison groups consisting of approximately one hundred houses with similar characteristics.26 The letter conveyed two messages. One was a list of concrete suggestions the homeowner could follow to reduce electricity consumption. The second message was a “social comparison module” that gave the household one of three ratings based on a comparison of its electricity use with the usage of other homes in the same group. Those who used less electricity than the average of the most efficient quintile of homes received a rating of “Great.” Those who used less than the average for all homes in the group got a rating of “Good,” while those who used more than the group average got a rating of “Below Average.”

Under this scheme, all homes in the highest decile of a group’s electricity consumption would of course receive a rating of “Below Average.” Relative to their own preexperiment baselines, people living in these homes reduced their consumption by 6.3 percent. By contrast, homes in a group’s lowest-usage decile all received a rating of “Great.” Usage went down even in these homes, but perhaps because residents were already taking advantage of the most effective conservation strategies, consumption in this group fell only 0.3 percent.27

As noted earlier, behavioral contagion is evident in decisions to adopt photovoltaic solar panels. The marketing professor Bryan Bollinger and the economist Kenneth Gillingham employed statistical methods like the ones discussed in chapter 7 to assess whether peer effects influenced solar panel adoption in a large sample of houses in California. After controlling for a variety of potentially important confounding influences, they estimated an even larger impact than earlier researchers had found for tobacco and alcohol use: a 1 percent increase in a zip code’s installed base of solar panels led to a slightly greater than 1 percent increase in the solar-panel adoption rate.28

In a similar study, Gillingham and the economist Marcello Graziano employed detailed data on solar installations in Connecticut. Here, too, adoption patterns exhibited considerable clustering that did not simply reflect causal influences like income. The probability that a homeowner in their sample would install solar panels was strongly influenced by the number of previously installed systems in the immediate vicinity. Consistent with the hypothesis of a contagion conveyed through social interaction and visibility, the authors found that the influence of nearby installations diminished with both distance and time.

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