American political-party affiliation as a predictor of usage of an adultery website

Kodi B. Arfer and Jason J. Jones
Created 11 May 2016 • Last modified 11 May 2017

More politically conservative Americans have more restrictive sexual attitudes. A natural follow-up question is how this difference in attitudes relates to actual behavior. But self-reports of sexual behavior may be compromised by a social desirability bias that is influenced by the very sexual attitudes at issue. We employed a non-self-reported measure of sexual behavior: usage of the adultery-focused dating website Ashley Madison. Linking an August 2015 leak of user data from Ashley Madison to 2012 voter registration rolls from five US states, we found 80,000 matches between 200,000 Ashley Madison user accounts and 50 million voters. According to simple rates in the sample, and also to predictively validated regression models controlling for state, gender, and age, we found that Democrats were least likely to use Ashley Madison, Libertarians were most likely, and Republicans, Greens, and unaffiliated voters were in between. Our results provide support for theories arguing that people with more restrictive sexual attitudes are paradoxically more likely to engage in deviant sexual behavior.

Keywords: adultery, online dating, political party affiliation, Democratic Party, Republican Party

Acknowledgments: This project was supported by the National Institute of Mental Health (T32MH109205).

Conflicts of interest: Both authors are registered to vote as Democrats. The authors declare that they have no other potential conflicts of interest.


In the United States, political attitudes and sexual attitudes vary widely, but tend to vary with each other. In general, more politically conservative Americans are also more sexually conservative, in both the attitudes they espouse and the behavior they self-report. Fried (2008), comparing Republicans and Democrats (the Republican Party being the more conservative of the two major US political parties) in General Social Survey (GSS) data, found that Democrats saw premarital sex as more acceptable, had had more sex partners, and were more likely to have had a male sex partner or participated in prostitution (whether buying or selling). Gallup (2003) found similarly that 80% of liberals but only 42% of conservatives saw premarital sex as acceptable. A meta-analysis by Whitley and Lee (2000) found that conservative views, as well as the related personality constructs of authoritarianism and social-dominance orientation, were associated with more negative attitudes towards homosexuality. Since more religious people (especially conservative Protestants) are more likely to be Republicans than Democrats (Pew Research Center, 2015), and religions (especially conservative Protestantism) tend to promote sexual conservatism, it is no surprise that more religious and more religiously conservative Americans tend to also have more negative views of premarital sex (Petersen & Donnenwerth, 1997), homosexuality (Hill, Moulton, & Burdette, 2004), and pornography (Carroll et al., 2008).

One might wonder how much conservatives' actual sexual behavior is in line with their attitudes, and how honest they are in reporting their behavior. Sex can be tempting, and people with stricter sexual standards should be all the less willing to admit their indiscretions to prying researchers. Some writers, inspired by Freudian ideas of sexual repression, or by sex scandals involving conservative public figures such as Republican senator Larry Craig (McArdle, 2007) and Pentecostal televangelist Jimmy Swaggart (Associated Press, 1988), have speculated that more sexually conservative people are paradoxically more sexually deviant in practice. Some support for this idea comes from two state-level analyses. Edelman (2009) found that states with more conservative attitudes towards religion, sexuality, and marriage buy more subscriptions per capita to pornographic websites. MacInnis and Hodson (2015) found that more religious states had more Google searches for the word "sex", and more politically conservative states had more Google Images searches for "sex".

In this study, we will focus on sexual infidelity, particularly adultery, which is reasonably common (17% of GSS respondents said they had had extramarital sex; Burdette, Ellison, Sherkat, & Gore, 2007) but widely frowned upon regardless of political orientation (a survey of 1,005 people found that 6% of conservatives and 9% of liberals found it acceptable; Gallup, 2003). More religious and more religiously conservative people are less likely to state that they or their spouse have had extramarial sex (Burdette et al., 2007; Tuttle & Davis, 2015; but contrast Atkins, Baucom, & Jacobson, 2001), as do Republicans compared to Democrats (Fried, 2008), and more politically conservative people estimate adultery in their marriages as less likely (Buss & Shackelford, 1997).

Our study obtained an objective, individual-level measure of adultery (more precisely, intention to cheat in one's own or another's committed relationship) by drawing on data from the 2015 leak (Victor, 2015) of Ashley Madison (AM;, a dating website that specializes in cheating (slogan until 2016: "Life is short. Have an affair."). Previous uses of this data in social science include Grieser, Kapadia, Li, and Simonov (2016) and Griffin, Kruger, and Maturana (2016). We linked AM users with voter registration records to determine how usage of AM could be predicted on the basis of political-party affiliation, gender, age, and state.

Data processing

All data-processing and analysis code can be found at We used two sources of data: voter registration rolls from five US states (California, Florida, Kansas, New York, and Oklahoma) in 2012, and records of AM users from the 2015 leak by a group calling itself the Impact Team. To determine whether each voter had used AM, we attempted to find a matching AM credit-card payment.

Processing the voter registration rolls

Regarding voter registration rolls, we chose states on the basis of what data was readily available and out of a desire to cover a variety of geopolitical groups in the US. We selected all voters in the data for whom the registration record was well-formed (e.g., had a field for each column) and at least one valid value for each of the four kinds of data items used for matching: first names, last names, 5-digit ZIP codes, and address numbers. (We used just the numeric portion of addresses so we did not have to manage issues of spelling and abbreviations.) We saved one of each of these kinds, except in the case of address numbers, of which we saved up to two. ZIP codes that did not lie in the state from which the voting records came (according to the database, which draws from United States Postal Service data) were considered invalid. When there were multiple records for the same numeric voter ID in the same state, we used only the one that appeared first in the voter data. Overall, we saved records for 48,852,975 voters. Table 1 compares the population of each state to the number of voter records we obtained.

Table 1. Summary information of the data by state. The first few rows show the number of voters registered in each party. Below that are comparisons of obtained voter records to population, and of AM users matched with voters to total AM users. The population figures are 2012 estimates from United States Census Bureau (2012).
State California Florida Kansas New York Oklahoma
Unaffiliated voters 2,710,719 2,700,397 522,094 3,016,645 229,494
Democrats 7,936,325 4,988,433 432,858 7,134,846 879,343
Republicans 5,297,443 4,378,861 773,346 3,484,717 844,305
Greens 114,126 6,143 0 32,811 0
Libertarians 110,808 12,016 11,690 4,710 0
Voters in other party 2,004,915 429,399 0 796,528 3
Total registered voters 18,174,336 12,515,249 1,739,988 14,470,257 1,953,145
Population 38,041,430 19,317,568 2,885,905 19,570,261 3,814,820
Percent covered 48 65 60 74 51
AM users matched with voters 32,457 17,928 2,642 21,776 2,072
Total AM users 92,058 43,987 6,282 54,203 6,603
Percent matched 35 41 42 40 31

In addition to this basic matching information, we saved each voter's political party, gender, date of birth, and date of registration. Not all of this information was available for all voters. Gender in particular was often missing (the Oklahoma records had no gender information at all), so we imputed it using known genders elsewhere in the voting data (for any state) on the basis of first name: when a large majority of people with known gender and the same first name were male or female, the missing gender was imputed to be the same. (Specifically, when p ≤ .05 or p ≥ .95, where p = (f + 1)/(n + 2), f is the number of females with the name, and n is the number of known-gender voters with the name (so p is the posterior mean of a Bernoulli distribution with a flat prior), we imputed the gender as male or female, respectively.) Overall, 18% of genders were missing, and we imputed 87% of these missing genders, leaving the final missing rate at 2.3%.

When date of birth was missing, we did not impute it. However, we discarded some dates of birth which were present but seemed unlikely to be correct. These included dates of birth on or after the date of registration; dates in the 21st century; impossible dates, such as September 31st or dates with month 0; and dates with unrealistically high concentrations of voters, such as 10 October 1942 in the Florida records. Across states, 1 in 68,000 birthdates were initially missing, but after discarding, 1 in 95 were missing.

A complexity of party affiliation arises from confusion about the term "independent". California, Florida, and New York have large numbers of voters (479,378, 339,002, and 557,885, respectively) registered to a party called an "Independent Party" or "Independence Party". These voters may have intended to indicate that they wished to be independent; that is, to not belong to any party. Independence Party of New York (2014) states "The Party's leadership recognizes that individuals do sometimes unwittingly register as members of the Independence Party when their intent was to register to vote as a 'blank'." Because there is no way to determine which of these voters meant to register as unaffiliated, we treat them all as part of the "other party" group, which is not included in the analyses.

Processing the AM data

We used three files from the AM leak. From CreditCardTransactions.7z, we obtained names, addresses, and ZIP codes for each credit-card payment on AM from 21 March 2008 to 28 June 2015. We selected only successful payment transactions with an Address Verification System code that indicated both the address and the ZIP code had matched against what was on record for the card. (Address Verification Systems do not check personal names in most cases.) We obtained 2,344,452 payments with US ZIP codes, which involved a total of 738,674 AM users, 203,133 of whom had ZIP codes for states in the voter data. From am_am.dump.gz and aminno_member.dump.gz, we obtained a list of AM users who had paid to delete their account. AM did not actually delete most of the information of each user who paid for such deletion, which was among the kinds of wrongdoing charged by the Federal Trade Commission for which an $18 million settlement was obtained from AM's owner, Avid Life Media, now called Ruby Corporation (Geuss, 2016).

A caveat of using AM's credit-card transaction data is that female users of AM weren't charged some of the fees that male users were (Lamont, 2016). Hence, our analyses will miss an unknown proportion of female users.


The matching algorithm worked by taking each user who made an AM credit-card payment and searching for a voter with the same first name, last name, and ZIP code, and at least one equal address number. If more than one voter satisfied these criteria for a given payment, none of the voters were treated as matching the AM user. On the other hand, if more than one AM user satisfied this criterion for a single voter, we treated this the same in our analyses as a unique match, since our only concern in the analyses is whether a voter used AM, not which AM account they used. We matched 78,296 voters to at least one AM user.

The analyses treated each voter as a subject, voter characteristics as independent variables, and whether the voter used AM as a dichotomous dependent variable. We expect that matching a credit-card transaction, as described above, is a good measure of whether a voter made a serious attempt to cheat via AM; by not including users who never paid money, we avoid including people who created an account but never attempted to cheat. An exceptional case is users who only paid to delete their account. These users may well have had an account created for them by somebody else to discredit them, and in any case, did not pay to cheat. Hence, we treated any matching voter identified by the above procedure as not using AM if they had exactly one credit-card payment and they paid to delete their account, which implies that the payment was for deletion. Among matching voters, 1,421 were treated as nonusers of AM for this reason, for an effective total AM usage rate of 76,875 out of 48,852,975 voters (1 in 635), several orders of magnitude below the self-reported adultery rate of 17% in the GSS (Burdette et al., 2007). Table 1 compares the number of AM users in each state to the number matched up with voters.


We present two basic analytic approaches: a simple approach that groups the sample into bins and counts frequencies, and a more complex approach based on logistic regression and cross-validation. Our analyses include subjects belonging to no party as well as to any of four parties, including the two major American parties, Democratic and Republican, and two of the largest minor parties, Green and Libertarian. Note that as shown in Table 1, not every state listed any voters registered as Greens or Libertarians.

The results of the simple approach are shown in Figure 1. We see that across all five states, Democrats have the least AM usage of all party groups, and in the four states where Libertarians are present, they use AM the most, with a rate of at least 1 in 300 in each state. Republicans and unaffiliated voters have intermediate rates that are similar to each other. Greens use AM more than Republicans in California and Florida, but less in New York. As for effects of state alone, ignoring parties (and hence including voters in parties other than these four), California has the most AM usage (1 in 560 voters), Oklahoma has the least (1 in 943), and the remainder are intermediate (1 in 659 for Kansas, 1 in 665 for New York, and 1 in 698 for Florida).


Figure 1. The proportion of registered voters who were matched to an Ashley Madison user, broken down by state and party. The y-axis is on a reciprocal scale, but upside-down so that greater rates are visually higher on the plot.

The analysis represented by Figure 1 is easy to understand, but crude, because it does not account for, for example, gender differences in party affiliation. In our data, 63% of Libertarians but only 42% of Democrats are male, and 1 in 301 men but only 1 in 8,686 women used AM, so it is worth distinguishing the effect of party from the effect of gender.

To estimate the predictive value of party above and beyond confounding variables such as age and gender, we investigated a family of logistic regression models, each regularized with an L2 (ridge) penalty. Logistic regression is an appropriate technique for modeling the probabilities of binary events, and regularization, also known as shrinkage methods, improves predictive accuracy by biasing regression coefficients towards 0 and hence countering overfitting. Our models treated each voter as a Bernoulli trial with the outcome being whether the voter used AM, and included state, gender, age, and party affiliation as predictors. Subjects with unimputed genders, missing ages, or parties other than the four analytic parties were removed, leaving a sample of 44,172,769 voters, 69,023 of which (1 in 640) used AM. State and party affiliation were dummy-coded, with New York and unaffiliated voters as the respective reference groups. Age was coded as the difference between the voter's year of birth and 2012, then standardized to have mean 0 and standard deviation ½ (Gelman, 2008), and supplemented with a similarly standardized age-squared term.

We considered six models. All nontrivial models were fit with Python 3.6 and Hy 4fce884, using the LogisticRegression class from scikit-learn 0.18.1. We used tenfold cross-validation to estimate the predictive accuracy of each model, randomly splitting the voter data into ten folds and then, for each fold in turn, fitting model parameters using the other nine folds and having the model predict the probability of using AM for each voter in the held-out fold. The L2 penalty size C was chosen among {10−12, 10−11, …, 1011, 1012} using inner rounds of fivefold cross-validation. We compared predicted probabilities to actual probabilities with mean squared error (MSE), also known in the context of probabilistic classification as the Brier score, which is a proper scoring rule (Brier, 1950; Bröcker, 2009).

Descriptions of each model and cross-validation results are shown in Table 2. By MSE, the models from least to most accurate are Trivial, Parties, Demo, IntDemo, DemoParties, IntDemoParties. Hence, not only does political party have predictive value on its own; it adds to the predictive ability of a model already accounting for age, gender, and state. Examining p0 and p1 helps to get a more concrete idea of this predictive performance. p0 is the mean predicted probability of AM usage among voters who did not actually use AM, so that a p0 of 1 in 500 would mean that the model predicted on average that nonusers had a 1 in 500 chance of using AM. p1 is the same value among voters who did use AM. Thus, more accurate models have smaller p0s but larger p1s. We see that p0 does not improve much in any case: the models are generally unable to identify nonusers as less likely to use AM than the base rate. However, p1 improves substantially as predictors are added to the model, and interaction terms also improve p1, although only slightly. We will emphasize DemoParties in interpretation, since it is almost as accurate as IntDemoParties while being substantially simpler, and our interest in this study is more in the overall effects of party affiliation than interactions of party affiliation with state, age, or gender.

Table 2. Results of the cross-validation analyses with the six models. "Terms" counts the number of terms in the model, including the intercept. p0 is the mean predicted probability of AM usage among voters who did not actually use Ashley Madison, and p1 is the same value among voters who did use Ashley Madison.
Model Description Terms MSE p0 p1
Trivial No predictors 1 0.001560127 1 in 640 1 in 640
Parties Predictors: party only 5 0.001559982 1 in 640 1 in 604
Demo Predictors: state, gender, age 8 0.001555791 1 in 642 1 in 232
DemoParties Predictors: state, gender, age, party 12 0.001555600 1 in 642 1 in 226
IntDemo Demo, plus all first-order interactions 22 0.001555783 1 in 642 1 in 231
IntDemoParties DemoParties, plus all first-order interactions 51 0.001555565 1 in 642 1 in 224

Having validated DemoParties and IntDemoParties as predictively accurate, we fit each model to all of the data (again using fivefold cross-validation to choose C) and examined the coefficients, which are shown in Table 3. We see that women are especially unlikely to use AM (since the coefficients are in logit units, the value −3.31 indicates that a male voter with a 50% chance of using AM would have his probability reduced to 3.5% if he were female); recall, however, that this effect is likely to have been inflated by women's underrepresentation in the AM credit-card data. In DemoParties, the terms for age describe a positive effect on AM usage from ages 25 to 63, peaking at age 44 with an effect of +1.04. Compared to New York, all states except California are associated with less AM use (except that IntDemoParties gives Florida a very small positive effect). In order from least to most AM-using, the parties are Democratic, unaffiliated, Green, Republican, Libertarian. This agrees with the simple analysis of Figure 1, with the possible exception of the position of the Greens, which is also inconsistent between DemoParties and IntDemoParties.

Table 3. Coefficients of the DemoParties and IntDemoParties models when fit to all of the data. The reference category for the party terms is unaffiliated, and the reference category for the state terms is New York.
Term DemoParties IntDemoParties
Intercept −6.20 −6.22
Age −1.07 −1.10
Age² −2.23 −2.14
Female −3.31 −3.13
Female × Age   0.52
Female × Age²   0.43
Female × Party: Democratic   0.18
Female × Party: Green   −0.08
Female × Party: Libertarian   0.03
Female × Party: Republican   −0.01
Female × State: California   −0.14
Female × State: Florida   −0.14
Female × State: Kansas   −0.23
Female × State: Oklahoma   −0.23
Party: Democratic −0.18 −0.17
Party: Democratic × Age   −0.13
Party: Democratic × Age²   0.08
Party: Green 0.02 −0.19
Party: Green × Age   0.22
Party: Green × Age²   −0.29
Party: Libertarian 0.46 0.47
Party: Libertarian × Age   −0.34
Party: Libertarian × Age²   −0.34
Party: Republican 0.19 0.28
Party: Republican × Age   −0.21
Party: Republican × Age²   0.05
State: California 0.14 0.16
State: California × Age   0.22
State: California × Age²   −0.19
State: California × Party: Democratic   0.06
State: California × Party: Green   0.23
State: California × Party: Libertarian   −0.15
State: California × Party: Republican   −0.16
State: Florida −0.04 0.01
State: Florida × Age   0.19
State: Florida × Age²   −0.22
State: Florida × Party: Democratic   −0.13
State: Florida × Party: Green   0.42
State: Florida × Party: Libertarian   −0.25
State: Florida × Party: Republican   −0.10
State: Kansas −0.10 −0.16
State: Kansas × Age   −0.33
State: Kansas × Age²   −0.59
State: Kansas × Party: Democratic   −0.07
State: Kansas × Party: Libertarian   −0.08
State: Kansas × Party: Republican   −0.25
State: Oklahoma −0.31 −0.15
State: Oklahoma × Age   0.23
State: Oklahoma × Age²   −0.14
State: Oklahoma × Party: Democratic   −0.26
State: Oklahoma × Party: Republican   −0.20

Table 4 provides an example to make the models' predictions concrete. The order of parties is the same between the two models except for, again, the position of Greens. We see that while all men of this age in New York are expected to have a higher rate of AM usage than the base rate (1 in 640), party affiliation can still make a big difference.

Table 4. Predicted probability of Ashley Madison usage for a 40-year-old man registered to vote in New York, broken down by party affiliation and model.
Party DemoParties IntDemoParties
Libertarian 1 in 117 1 in 98
Republican 1 in 152 1 in 138
Green 1 in 180 1 in 219
unaffiliated 1 in 184 1 in 189
Democratic 1 in 219 1 in 223


Using two analytic strategies, one simple and one complex, we found that a registered voter's probability of spending money on AM for something other than deleting their account—hence, apparently intending to cheat in a romantic relationship—varied substantially based on their political party. Libertarians were most likely to use AM, Democrats were least likely, and Republicans, Greens, and unaffiliated voters were in between. This pattern mostly coincides with political conservatism, with members of more conservative or more right-wing parties using AM more often.

Our results are perhaps the strongest evidence yet that people with more sexually conservative values, although they claim to act accordingly, are more sexually deviant in practice than their more sexually liberal peers. If this is true, what could explain it? We are skeptical of Freudian repression and other concepts of unexpressed sexual desire "boiling over", because the evidence for a quasi-hydraulic sex drive like this is weak at best (Arfer, 2016) despite longstanding keen interest in this idea. We suggest the blame lies not with attempting sexual self-control in the first place, but with the other attendant circumstances of sexual conservatism, such as reduced knowledge of sexuality (Coleman & Testa, 2008) due to weak or nonexistent formal sex education (Kirby, 2007; Stanger-Hall & Hall, 2011) and less forthright discussion of sexual matters with friends and family. It would make sense if less sexually knowledgeable people were worse at sexual self-control. More religious people may also have difficulty with sexual self-control if they attempt to rely on supernatural help to restrain their impulses.

Our study has two notable strengths in its very large sample and its avoidance of self-report and informant report. On the other hand, many limitations apply to its conclusions. First, while we would have liked to capture all cheating events, our data can only distinguish between users and nonusers of AM. There is no way to tell how much of the variation we observed is due to actual variation in cheating, and how much is due to variation in means of obtaining people to cheat with; comparing the GSS to our data implies that only a tiny minority of cheating events are aided with AM. Second, the algorithm we used to match AM users and voters cannot be perfectly accurate, and we do not know how accurate it is. It is at least strict enough that misses are likelier than false positives. Third, party affiliation is contaminated with some other variables we would have liked to control for but we lacked data for, such as marriage: married people are probably likelier to use AM, and Republicans are likelier to be married than Democrats (Fried, 2008). Controlling for age does at least help ameliorate our inability to control for marriage. Finally, the lack of existing research on sexual attitudes and self-reported sexual behavior among Libertarians and Greens hinders the interpretation of our results for them.


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