100 Neighbors on the Bill: What Peer Comparison Did to 588,446 Households
Home energy reports compared each household with 100 neighbors. The data show a 2.0% average cut, a backfire among thrifty homes, and savings that fade slowly once the mail stops.
In this article7 sections
The mailing compares one household’s electricity use with that of 100 neighbors who have similar square footage and the same type of heating, then adds some advice on using less. In the largest analysis of it, which J-PAL summarizes as covering 588,446 households at twelve US utilities, average consumption fell by 2.0%, or 0.62 kilowatt-hours per household per day (J-PAL’s summary of Allcott and Rogers, American Economic Review, 2014).
Peer comparison on a utility bill changes behavior modestly and durably: households receiving home energy reports cut electricity use by 2.0% on average, the effect was largest for the heaviest users (6.3% in the top decile of prior consumption versus 0.3% in the bottom decile, Allcott, Journal of Public Economics, 2011), and households that stopped receiving reports after about two years kept saving, with the gain decaying by roughly 10 to 20 percent a year. The same kind of comparison, delivered as a bare average without an approval signal, made some thrifty households use more energy (Schultz et al., Psychological Science, 2007).
The bare-average result is the reason to read the whole record rather than the headline.
How big is 2.0%, and who produced it?
Hunt Allcott’s 2011 paper used randomized field experiments covering about 600,000 treatment and control households across the United States and found that the average program reduced consumption by 2.0%. He compared that with the effect of a short-run electricity price increase of 11 to 20 percent. A mailing moved usage about as far as a large price rise would have.
The scale is easy to underestimate. J-PAL reports that by 2012 about 6.2 million households served by 85 utilities received the reports. Multiply the 0.62 kilowatt-hour daily average by 6.2 million households and 365 days and the result is roughly 1.4 billion kilowatt-hours a year. That figure is back-of-envelope arithmetic from a sample average, not a measured national total, but it shows why a 2% effect is taken seriously.
The average hides a lopsided split. Households in the top decile of earlier consumption cut use by 6.3%. Households in the bottom decile cut it by 0.3%. The people who gain least from a comparison with neighbors are the people who were already using least.
The 2014 follow-up with Todd Rogers widened the lens to twelve utilities. According to J-PAL’s summary, reductions appeared at every utility, ranging from 1.4% to 3.3%, and the reports did not increase consumption at any utility. Monthly reports beat quarterly ones by 0.5 percentage points. Rebates for home improvements explained little of the saving, which led the researchers to suggest that behavior, such as thermostat settings, rather than purchases drove it.
Why did a bare average backfire in San Marcos?
A smaller field test by Wesley Schultz and colleagues in San Marcos, California, found the catch. They told 290 households how their electricity use compared with the neighborhood average and then measured what the households did next.
Households that started above the average used less afterward. Households that started below the average used more. The authors called the second result a boomerang. A number meant to motivate worked as permission — if the neighborhood typically uses more than I do, my restraint is optional.
The repair was a message of approval. Schultz and colleagues separate a descriptive norm (what most people do) from an injunctive norm (what people approve of). When the information about the average was paired with an injunctive signal, the below-average households held steady instead of rising, and the above-average households still cut back, according to summaries of the paper from The Decision Lab and the Center for Community-Based Social Marketing.
The large mailings did not show the boomerang in aggregate: J-PAL reports no increase at any utility. Why they avoided it is unresolved. What the San Marcos result establishes is the risk, which falls on people already doing well.
What happens when the mail stops?
Persistence is the part that made the 2014 paper more than a replication. Allcott and Rogers followed households that were dropped from the program after about two years. According to J-PAL’s summary, those households kept saving roughly 0.6 kilowatt-hours a day more than the control group, about 2% of their electricity, and the AER abstract puts the later decay at 10 to 20 percent a year. Households that kept receiving reports saved 50 to 60 percent more than the dropped ones.
Inside each year, the pattern looks like a tide. Savings spiked when a report landed and decayed until the next one. The authors’ wording for the early years is a cycle of action and backsliding, which weakened with time. They also found that households were slow to habituate: people continued to respond to repeated treatment even after two years.
Two kinds of durability are tangled together here. One is a change that survives removal of the prompt, which the dropped households show. The other is the repeated prompt itself, which the continuing households show by saving more. Both are true in the same data.
A four-dial way to read the evidence
The following is this article’s own tidy-up of the studies above, not something the authors propose. It has four dials, each with a finding attached.
| Dial | What it controls | Evidence |
|---|---|---|
| Position | Where the person stands against peers | Top decile cut 6.3%, bottom decile 0.3% (Allcott 2011) |
| Norm | What the average is taken to mean | Below-average households rose when given the average alone (Schultz et al. 2007) |
| Approval | Whether low use is marked as good | Adding approval stopped the rise (Schultz et al. 2007) |
| Recurrence | How often the signal returns | Monthly beat quarterly by 0.5 points; dropped households decayed 10 to 20% a year (Allcott and Rogers 2014) |
Position decides who responds. Norm decides which direction the response goes for people near or below the middle. Approval decides whether the below-average group hears that restraint is valued or merely typical. Recurrence decides whether any of it lasts. A program that turns one dial and ignores the others produces odd results, and San Marcos is what turning only the norm dial looks like.
What does this predict for friends comparing wake-up times?
What follows is inference from electricity data.
Take six friends whose usual alarms fire at 6:00, 6:20, 6:30, 7:00, 7:15 and 7:35. These numbers are invented for illustration. The group average is 6:47. A board that headlines “the group wakes at 6:47” tells three people they are ahead of it and three that they are behind. The San Marcos result says the three early risers are the ones at risk of drifting toward 6:47, because the average has told them they have slack. The three later risers get the useful push.
An approval signal would be the injunctive half of the repair. In a friend group that rarely looks like a smiley face; it looks like an agreement made out loud, such as “we all said 6:30 is the goal, and early is good.” The San Marcos result suggests the agreement matters most to the people the average would otherwise relax, the six-friend group’s 6:00 and 6:20 alarms, who need to hear that being ahead is valued and not merely typical. That reading is a hypothesis. The home-energy data cannot say whether spoken approval works as well as the cue Schultz and colleagues used.
Matching is the first fix. The reports compared a household with 100 neighbors of similar size and heating type. A board that puts a nurse on rotating shifts next to a baker who starts at 3 a.m. compares no one with anyone, so peer groups need comparable wake-up demands.
Dropping the average is the second. Showing each person’s own record to people who care about it avoids the headline number entirely.
Recurrence is the dial people forget. The electricity data say a single strong message fades and a repeated one keeps working. A one-time group challenge in January resembles the quarterly mailing, not the monthly one. How well nudges hold up overall is itself disputed; see the argument over publication bias in nudge meta-analyses.
Live rankings add position and recurrence while removing matching, so the outcome depends on who is in the ranking. A teardown of a leaderboard-and-streak product covers the fitness version of that trade.
What these studies cannot say
Four limits are worth keeping in view. The subjects were utility customers, a different group from people who download a self-improvement app. The outcome was kilowatt-hours, which a meter records without anyone’s cooperation, while a wake-up time can be misreported. The headline effect is 2.0%, which matters across millions of homes and may be invisible to any one household. And the decay figure of 10 to 20 percent a year is the authors’ estimate, not a count of how many houses went back to old habits.
Quick answers
Does comparing people to their neighbors change their behavior? Yes, by a small amount that lasts. J-PAL’s summary of Allcott and Rogers (American Economic Review, 2014) reports 588,446 households at twelve US utilities and an average 2.0% reduction in electricity use, with every utility between 1.4% and 3.3%. Each report compared a household with 100 neighbors of similar square footage and heating type and added saving tips. Households that kept getting reports after two years saved 50 to 60 percent more than households that were dropped.
What is the boomerang effect in social norm messaging? It is the finding that telling people the average can pull below-average performers toward the average in the wrong direction. In the Schultz, Nolan, Cialdini, Goldstein and Griskevicius field test (Psychological Science, 2007), households among the 290 studied that were told the neighborhood average and started below it used more energy afterward. Adding a message of social approval removed the increase.
Why did the effect fade between reports? Allcott and Rogers describe a cycle of action and backsliding: consumption fell when a report arrived and drifted back up until the next one, and the cycles weakened over time. Monthly reports cut consumption by 0.5 percentage points more than quarterly ones, which fits that picture. Rebates for home improvements explained little of the saving, which points to everyday behavior such as thermostat settings.
Would a wake-up time leaderboard work the same way? Untested. The energy studies suggest three cautions for any ranked or averaged board: compare people to genuinely similar peers, pair any average with a signal of approval, and expect the effect to need repeating. Those are inferences from electricity data, not findings about mornings.