A Closer Look by the ECPIP Staff… Gender Effects in the 2013 NJ Gubernatorial Election?

The Candidate Gender Effect That Wasn’t?
The Subtle Role Gender Played in the 2013 New Jersey Gubernatorial Election

Ashley Koning

Ashley Koning is Manager of the Rutgers-Eagleton Poll and a Ph.D. candidate in Political Science at Rutgers University.

The past election cycle in New Jersey was notable for many reasons, not least of which was that State Sen. Barbara Buono was the first woman to run for the executive office as a major party candidate since Gov. Christie Todd Whitman more than a decade ago.  It initially seemed like the governor’s race could have been the perfect storm for a gendered campaign, given Buono’s stark contrast to Gov. Christie – the very embodiment of “Jersey guy” toughness – and her partisanship, ideology, and agenda that revolved around the types of compassion and social issues frequently stereotyped as feminine, “communal,” and emotionally charged.  After all, we know through a whole host of research that the entire process is gendered for women candidates from selection through Election Day as they continue to be stereotyped – even if not intentionally – by both voters and by the media (see the Center for American Women and Politics or the Political Parity project for more on this).  Gov. Whitman even speculated decades ago that her gender affected pre-election polling during her run for governor back in 1993, consistently underestimating her eventual win (though this has been debated in the literature, both for and against).

Yet it was Gov. Christie who cried foul about his physical appearance being put in the campaign spotlight after a comment he claimed Buono made that alluded to his weight issues.  And the only gender gap the media spoke of was the one in Christie’s favor among women voters.  In a race where gender could have potentially had a significant impact, any type of gender effect from Buono’s candidacy seemed to be non-existent.

But sometimes there is more than meets the eye in these cases.  Therefore, in our final pre-election poll from October 28 – November 2, we conducted a small “list” experiment to test whether Buono’s gender actually had any effect on why respondents thought she was behind in the polls throughout the entire campaign.  For a campaign season that rarely – if ever – explicitly mentioned Buono’s gender as a pro or con to her candidacy, the results of our survey experiment paint somewhat of a different story.

First, what is a “list experiment”?

List experiments have increasingly become a popular research tool in political science as a way to unobtrusively tease out attitudes towards sensitive subjects that respondents may not want to express explicitly – whether because of a wish to sound socially desirable and not go against commonly held societal beliefs, to maintain congruency within oneself regarding other views and aspects of his or her own life, or in order to not disparage the interviewer based on perceived characteristics such as gender and race.  Respondents only become more comfortable expressing socially undesirable or unacceptable answers when a degree of anonymity can be obtained.  Therefore, survey list experiments enable this desired anonymity by allowing respondents to not have to directly state their views to the interviewer but rather provide a much more coded and private – and thus hopefully more accurate – response.

In a list experiment, the survey sample is randomly split into different groups; one group acts as the baseline or “control” condition group, while the one or more remaining groups act as the experimental condition(s) to test the sensitive issue in question.  All groups are then given a list and asked only to specify how many items on the list they would choose; those in the experimental group(s) have one additional item on their list related to the target issue than those in the control group.  Respondents then report only a number based on how many items they support or oppose, never specifying exactly which item(s) was (were) left out.

For example, we asked the following question to more subtly explore the impact of gender on Buono’s campaign:

“There has been a lot of speculation about why Barbara Buono has been so far behind Chris Christie in the polls during this entire election season.  I am going to read you three possible reasons for it. I would like to know how MANY of these reasons you agree with.  Just tell me how many, I do not need to know which ones.”

 We then split the sample in half, randomly assigning half of our respondents to each group.  The first half were given three statements about Buono in a random order and asked whether they agreed with none, one, two or all three of them:

Her views are too liberal for New Jersey.
She is not well known throughout the state.
She did not receive enough support from the Democratic Party.

The other half of the sample received the same three statements, plus a fourth statement that addressed Buono’s gender.  All four were presented in a random order, and these respondents were asked whether they agreed with none, one, two, three, or all four of them. The fourth statement was:

            She is a woman.

Since the ONLY thing that differs between the two lists is the addition of the target item in the second list, we can use some very simple statistics to see if it ha any effect, that is, to see if  some voters think Buono was disadvantaged by being a woman. Because the two groups are random, if both got the same list we should get about the same mean number of responses for both groups. But since the second group has an extra item – the target item – any difference in the mean between voters in the second group and those in the first has to be because of the extra item.

So we compare the means (averages) of both groups, and look at the difference between the first group (the “control” list) and the second group (the “experimental” list) to determine the percentage of respondents who supported or opposed the target item.  While we will not know whether individual respondents agreed or disagreed with this item, we will get a more accurate read on  how many voters felt Buono’s gender was a disadvantage than we would if we asked the question outright..

So, was there a candidate gender effect?

The short answer is yes, but a modest one.  The first “control” group that received only three items selected 1.77 items on average.  The second group – our “experimental” group – that received the four items selected 1.92 items on average.  We take the difference of these two averages and multiply it by 100 to get the percentage of respondents who actually agreed that Buono’s gender influenced her continual double-digit lag behind Christie in the polls throughout the campaign. The result is 15 percent of NJ voters thought Buono being a woman candidate contributed to her support deficit during the race.

We know that there are no significant differences in characteristics between the two random groups – such as respondent gender, age, race, partisanship, ideology, and vote choice.  Furthermore, the only difference between the experimental versions assigned to these two groups is the item specifying Buono’s gender; all other items are the same, and all items are presented in a random order.  Therefore, we can with a great deal of confidence say that this extra gender item is making the difference – and according to tests of statistical significance, this difference is not just by chance.

The Candidate Gender Effect That Not-so-evidently Was

While 15 percent may not initially seem like a large amount, that is about one in seven voters who believed Buono’s gender to be an issue in her performance during the campaign.  If we generalize this a bit to the more than two million New Jersey voters who voted for governor this past election, that means more than 300,000 voters may have believed in some way that Buono being a woman candidate contributed to her struggles.  While Buono no doubt eventually lost for a large variety of reasons – Christie’s untouchable and skyrocketing post-Sandy popularity, lack of support from Democrats at the elite and mass level, and difficulties fundraising compared to Christie’s insurmountable war chest just to name a few – her gender may be one of them despite the apparently non-existent role it seemed to play publicly.

Buono in fact did a few points better in the actual election results than in the last pre-election average forecast by Real Clear Politics, and though Christie did slightly better than the average predicted as well, Buono’s increase from the average to the actual was twice Christie’s (Christie went up 1.6 points from an average of 58.8 percent to an actual of 60.4 percent; Buono went up 3.4 points from an average of 34.7 percent to an actual of 38.1 percent).  Perhaps there is some more support here for the polls underestimating Buono in part due to gender, but the clearest takeaway here is that gender still plays a notable role in campaigns – even when it seemingly does not.

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Filed under 2013 NJ Election, Buono, Chris Christie

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