The wage gap between the sexes in America has been narrowing much faster than observers ever realized, although this revelation by a pair of University of Georgia researchers isn’t as good a tiding as it could be. Jeremy Reynolds and Jeffrey Wenger, who have stumbled upon a quirk in existing survey data that could also color how we measure all types of other sociological trends, say statisticians have been as much as 50 percent off in tracking the progress of women’s wages in the work force.
“But that’s only because things were worse in the past than we had realized,”Reynolds said. “We’re not saying that women all over the U.S. should be rejoicing because it turns out the gender gap today is smaller than we thought. We’re saying something more like, ‘Women have come further than we realized.’”
So how do they know this when years of data from the Census Bureau’s Current Population Survey have said otherwise? The short answer is that many respondents have not been answering the income question accurately — which is a judicious, academic way of saying that some of these folks have been, well, fibbing.
Reynolds and Wenger have identified an intriguing set of biases baked into the existing data on the gender wage gap: people tend to underestimate the wages of others and overestimate their own. Men, in particular, are really bad about this.
The authors, whose study will be published in the journal Social Science Research, identified these biases by examining data from the Current Population Survey. Respondents are interviewed multiple times, one year apart. When the researchers looked at how responses to these questions changed across the subsequent interviews (controlling for other factors), they found that people answered more generously for themselves than other people had for them.
About half of the data on this income question in the Current Population Survey have long come from “proxy reporters” — people answering on behalf of others in their household. In the early ’80s, a majority of these proxy reporters were women. “They were simply around to answer the phone call,” Reynolds said, noting that women had not entered the work force full time back then to the extent that they have today.
On the whole, these female survey respondents likely under-reported the income of their husbands, and over-reported their own — creating the skewed impression that the gender gap in America was much smaller in the early ’80s than it really was.
Reynolds and Wenger suspected gender might affect reporting, too. In parsing the data that way, they discovered that men in particular inflate their own incomes when asked.
“From a sociological perspective anyway, that seems to make a lot of sense,” Reynolds said. “Despite changing gender roles, men still are seen by others and maybe by themselves in many cases as the breadwinners of the family. They may feel like it makes them look better, it secures their masculinity if they’re able to report higher wages.”
He and Wenger can’t say for sure that this is what’s going on (and it may be a separate subject for study why anyone would want to bluff about their paycheck to an otherwise anonymous survey-taker). But the theory sounds reasonable.
“When women’s wages are under-reported by men, that would also be consistent with the argument in sociological literature about the systemic bias that people have when they report women’s earnings, or women’s capabilities,” Reynolds said. “They often underestimate them.”
It’s possible some of these proxy reporters weren’t really biased. Maybe they honestly didn’t know how much their spouses made. But, as Reynolds puts it, it’s a little suspicious that people tend to universally over-report their own income while under-reporting the earnings of other people. If sheer ignorance were really the source of the discrepancy, the trend wouldn’t be so neat.
Once Reynolds and Wenger had calculated the extent of these biases, they went back to the data we’ve long used to measure the wage gap and readjusted it. Over time, as more women have entered the labor force, men have also become more likely to answer these surveys for themselves. And that impacts the data, too. The existing analysis — based on what the authors call the “naïve approach” to this data — suggested that the wage gap in America between 1979 and 2009 closed by about 16 percent (or $1.19 per hour). Wenger and Reynolds put that number instead at 22 percent (or $1.76). And so we have been 50 percent off in this basic calculation.
That’s a huge number, and it implies that we may want to apply this new understanding of reporting biases to how we’ve measured everything from the racial wage gap to survey data on educational attainment or even criminal behavior.
Because “it’s so big,” Reynolds said, he and Wenger would be “super surprised” to see it not affecting conclusions in other fields.