Is Social Status Related to Internet Pornography Use? Evidence from the Early 2000s in the United States

Abstract

While most studies on Internet pornography focus on individual's psychological characteristics, few have explored how social status itself is associated with Internet pornography use. As the Internet is becoming increasingly prevalent, online behaviors may have started to reflect the inequalities of the offline world. This study tested whether lower social status was associated with fewer sexual intercourse opportunities, and whether this led to higher likelihood of using Internet pornography as an alternative means of sexual release. To test the theory, I used the nationally representative sample of the General Social Survey of the U.S. between 2000 and 2004, with missing data handled by chained multiple imputation. The analyses found that lower income, longer working length, being unemployed, or a laborer in the social class strata were associated with fewer sexual intercourse opportunities as measured by three variables: marital status, the number of sex partners, and sex frequency. Lower income, less education, and longer working length were also associated with higher odds of using Internet pornography in the past 30 days, but only income was partially mediated by marital status. Social status was associated with Internet pornography use and sexual intercourse opportunities independently. The comparison of Internet pornography with the traditional X-rated movie found the unique features of Internet pornography use absent for X-rated movie.

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Archives of Sexual Behavior
The Official Publication of the
International Academy of Sex Research
ISSN 0004-0002
Arch Sex Behav
DOI 10.1007/s10508-015-0584-3
Is Social Status Related to Internet
Pornography Use? Evidence from the Early
2000s in the United States
Xiaozhao Yousef Yang

1 23
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ORIGINAL PAPER
Is Social Status Related to Internet Pornography Use? Evidence
from the Early 2000s in the United States
Xiaozhao Yousef Yang
1
Received: 13 June 2014 / Revised: 9 June 2015/ Accepted: 17 June 2015
ÓSpringer Science+Business Media New York 2015
Abstract While most studies on Internet pornography focus
on individual’s psychological characteristics, few have explored
how social status itself is associated with Internet pornography
use. As the Internet is becoming increasingly prevalent, online
behaviors may have started to reflect the inequalities of the
offline world.This study testedwhether lower social status was
associated with fewer sexual intercourse opportunities, and
whether this led to higher likelihood of using Internet pornography
as an alternative means of sexual release. To test the theory,I used
the nationally representative sample of the General Social Survey
of the U.S. between 2000 and 2004, with missing data handled by
chained multiple imputation. The analyses found that lower
income, longer working length, being unemployed, or a laborer
in the social class strata were associated with fewer sexual
intercourse opportunities as measured by three variables: marital
status, the number of sex partners, and sex frequency. Lower
income, less education, and longer working length were also
associated with higher odds of using Internet pornography in
the past 30 days, but only income was partially mediated by
marital status. Social status was associated with Internet pornog-
raphy use and sexual intercourse opportunities independently.
The comparison of Internet pornography with the traditional X-
rated movie found the unique features of Internet pornography
use absent for X-rated movie.
Keywords Internet pornography Social status 
Sexual intercourse General Social Survey 
Multiple imputation
Introduction
Internet pornography has become popular among Americans since
the high-speed Internet has enabled the convenient and low-cost
consumption of virtual sex. Today, a significant proportion of peo-
ple have used online pornography (Fisher & Barak, 2012); a small
proportion that use it excessively could even qualify as needing fur-
ther investigation for addiction (Greenfield, Orzack, & Cooper,
2002; Griffiths, 2001). Amidst the debate on pornography and sex-
ual deviance, a common perspective has assumed pornography use
is largely a voluntary behavior that constitutes an independent vari-
able leading to other deviances (e.g., rape, voyeurism). It also
appears as a behavioral consequence of other media consump-
tion (Barak & Fisher, 2002; Cooper, 1998; Stein, Black, Shapira,
& Spitzer, 2001).
There are many studies conceptualizing Internet pornog-
raphy use based on individual psychological dispositions
(Paul, 2009;Seto, Maric, & Barbaree, 2001;Stein et al., 2001).
Buttomyknowledge,few have examinedhowsocialstatuscan
influence pornography use. A recent meta-review by Short,
Black, Smith, Wetterneck, and Wells (2012), for example, has
highlighted the paucityof research on the social antecedents of
Internet pornography consumption.
This study sought to test the hypothesis that social status is
associated with Internet pornography consumption in the
United States, even after controlling for the effects of basic
demographic characteristics, social bonds, and opportunity
factors. I used the General Social Survey from the year 2000
and 2004 to answer this research question. The same models
for Internet pornography use were also applied to watching
X-rated movies in order to compare whether watching Internet
&Xiaozhao Yousef Yang
yang337@purdue.edu
1
Department of Sociology, Purdue University, 700 W. State St.,
West Lafayette, IN 47906, USA
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DOI 10.1007/s10508-015-0584-3
Author's personal copy

pornography differs from watching erotic materials via tradi-
tional media.
Opportunity to Access the Internet
Many studies found that by the early 2000s in the U.S., a majority
of the population already had access to the Internet (Brodie et al.,
2000; Cetron & Davies, 2005; Ybarra & Mitchell, 2005), as the
gap was rapidly narrowing from the late 1990s (Ono & Tsai,
2008). Even when the digital gap existed, the poor and the well-
offs differed in the content of internet use rather than the ubiq-
uitous usage (Brodie et al., 2000;Graham,2008;Keegan,2004;
Peter & Valkenburg, 2006).
At the same time when the digital divide vanished, the manner
of using the Internet differed from traditional media. The advan-
tages in lower price, anonymity, and accessibility have made
Internet pornography a convenient and low-cost channel com-
pared with traditional pornographic media such as a theater,
magazine, or DVDs. Because of these advantages, its user group
spread beyond the self-initiated active users to include many
involuntarily users and passive adopters. From national repre-
sentative samples, scholars found 66 % of who viewed Internet
pornography were doing so involuntarily (Wolak, Mitchell, &
Finkelhor, 2007), and over90 % of boys and 60 % of girls were
ever exposed to Internet pornography (Sabina, Wolak, & Finkel-
hor, 2008),indicating the accessibility to Internet pornography
is unprecedented in the U.S. The trend was similar in other
developed countries asof the early 2000s (Luder et al.,2011).
Opportunity factors such as access to the Internet, computer
knowledge, and concerns of anonymity thus may affect Internet
pornography use differently from using traditional porno-
graphic media.
Social Status
One study summarized that‘‘when the Internet matures, it will
increasingly reflect known social, economic and cultural rela-
tionships of the offline world, including inequalities’’ (van
Deursen & van Dijk, 2014). As the disparity of accessing the
Internet is quickly narrowing or even disappearing in the devel-
oped countries like the U.S., I hypothesize that the likelihood of
using Internet pornography relies not primarily on the opportu-
nity to access the Internet, but is associated with one’s social status
even after controlling for internet accessibility and demographic
background.
Social status influences the likelihood of having sexual inter-
course opportunities, and the lack of them may urge people to
seek compensating means of sexual releases such as masturba-
tion and watching pornography. Social exchange theory (Blau,
1964;Sprecher,1998) conceptualizes sexual intercourse oppor-
tunities as structured by the unevenly distributed resources and
social positions. Assuming individuals are rational actors and
each will maximize the reward/cost ratio,social exchange the-
ory depicts a dynamic process in which the ranks of social sta-
tus translate into sexual intercourse opportunities. With less
available material (money, physical attraction) and symbolic
(education, prestige, etc.) resources, a person of lower status
is more likely to fail the competition for a sexual partner and
will either retreat entirely or repeat the competition with a
lower status target and expect a better fit. Even when such
person does win a sexual partner of much higher status, his/her
cost of maintaining this sexual opportunity may gradually
exceed the reward, and consequentially becomes more likely
to repeat another round of selection from the lower status
targets (Edwards, 1969; Lawrance & Byers, 1995). It is well
known that people with more resources and power are better
able to negotiate for sexual opportunities (Buss, 1989; Fein-
gold, 1992; Wiederman, 1993). Moreover, people of lower
status are not just likely to fail a game for sexual opportunities,
often they consequently pair up with less-desired mates, who
also tend to provide less sexual reward (Lawrance & Byers,
1995). This explains how the social exchange process distributes
favorable sexual intercourse opportunities to the higher-ranking
individuals, even among animals. Scientists found that social
ranking reflected by grooming order, strength, body shape, and
leadership determines a primate’s chance of mating and passing
genes (Berard, Nurnberg, Epplen, & Schmidtke, 1994;Sapol-
sky, 2005; Soltis, Thomsen, & Takenaka, 2001). Lower ranking
primates were more likely to masturbate, and comparably more
frequent when they do so, according to several observations (Dubuc,
Coyne, & Maestripieri, 2013; Hanby, Robertson, & Phoenix, 1971;
Thomsen & Soltis, 2004). Scientists have also recorded cases
of quasi-pornographic presentations among the non-human
primates of lower ranking: primates masturbating to the sight
of caged females (Seelye, 1966; Thomsen & Soltis, 2004).
Being less competitive in the conventional sex market, some
may turn to Internet pornography because it involves lower exchange
costs while still providing sexual release. Therefore, people with
lower social status, according to the social exchange theory,were
more likely to resort to alternatives such as Internet pornography
to compensate the lack of sexual intercourse opportunity or hav-
ing a mate with lower desirability (and thus lower reward) (Gut-
tentag& Secord, 1983;South,1991).Young (2008) has identified
that the anonymous and accessible nature of cybersex, pornog-
raphyincluded, had attra cted pe ople wi th low s elf-e steem, poor
social skills, and limited interpersonal communication experi-
ences to seekcompensation from thevirtual sex. Zimbardoand
Ducan (2012) refered to the reliance on pornography for arousal
and lack of social skills with women among the contemporary
men as‘‘the demise ofguy.’’Clinicians have reported a growing
number of Japanese youth who avoid intimate relationships
but seek a variety of digital sexual activities (Haworth, 2013).
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Social Bonds
The social control theory hypotheses that social bonds to con-
ventional institutions reduce deviance from traditional norms
such as the sanction against pornography use (Hirschi, 1969).
For example, people who are religious are more likely to inter-
nalize the condemnation message on pornography and use
pornography less (Hayes, 1995; Stark & Bainbridge, 1997).
Yoder, Virden, and Amin (2005) reported the association between
loneliness and Internet pornography use. Ybarra and Mitchell
(2005) found that adolescents who deliberately seek X-rated
materials are less emotionally connected to their parents. Others
have similarly found that frequent pornography users have
fewer ties to important social institutions, especially family,
marriage, and religion (Mesch, 2009; Stack, Wasserman, &
Kern, 2004).
At the same time, social bonds and social status are two closely
related factors. Higher status may come as a consequence of build-
ing relationships with people who hold power in conventional
institutions; on the other hand, social status can serve as a
doorknocker for entering socially bonding scenarios (Lin, 2002;
Wuthnow,2002). Therefore, it is essential to control for the effects
of social bonds when social status is under scrutiny.
The Current Study
I hypothesize that there is a negative relationship between social
status and Internet pornography use even after controllingfor
social bonds and internet use opportunities. Because Internet
pornography may be utilized as a compensation for the lack of
sexual intercourse opportunity, the relationship between
Internet pornography use and social status is mediated by the
chance of having sexual intercourse. Social bonds are often con-
tingent on social status. Therefore, this study also tests whether
the associations between various social status indicators (i.e.,
income, education, subjective and objective class locus, work
length) and Internet pornography use would become less sig-
nificant or smaller in magnitude after controlling for the social
bonding effects (religious attendance, attitude to pornography
law, fundamental beliefs, and conservatism).
The mechanism for Internet pornography to serve as an alter-
native of sexual release for people with lower social status relies
upon the assumption that opportunity factors (anonymity, cost,
Internetprevalence) do notimpedepeople with lowersocialstatus
from consuming Internet pornography. Such opportunity fac-
tors may be unique to online behaviors but not so for offline
conducts (e.g., to purchase an X-film), as the latter involves a
different level of cost, technology competency, and anonymity.
To compare Internet pornography use and the use via tradi-
tional media (X-rated movies), this study applies the same
set of models to both dependent variables.
Method
Subjects
This studyutilized the General SocialSurvey (GSS) of the U.S.
from year 2000 to 2004 as the resource of testing our hypothe-
ses. The GSS isthe largest nationally representative full-prob-
abilitysurvey conducted in the U.Severy two years, which sur-
veys non-institutionalized adults in the U.S. through face-to-
face interviews by an equal-probability sampling process clus-
teredby stateand county(Davis,Smith,& Marsden,2007).The
GSS is known for its comprehensive measurement of social and
attitudinal variables of various types, and its reliable represen-
tativeness of the larger U.S. society achieved by a well-
designed interview procedure. The GSS team had collected the
dependent variable of main interest—Internet pornography use—
during years 2000, 2002, and 2004, but not in subsequent surveys.
The dependent variable for the comparison model, watching X-
rated movies, is also available for the same survey years.
I employed multiple imputation to handle missing data for
independent variables, and analyze the section of sample where
complete data for the outcome variables is available. The dataset
was imputed on complete outcome variables because the infer-
ence will be biased when imputing the missing in dependent
variable to predict independent variables (Allison, 2000).
Fortunately, the rotation design of the GSS has randomized
the respondents with a ballot-split method for each year-
limited rotation section, which also includes the needed depen-
dent variables: Internet pornography watch and X-rated movie
watch(NORC, 2012). For eachsurvey year, respondents recruited
to answer these specific questions were randomly assigned and
chosen from theentirepool of respondents; thus, the sample left
out from answering these questions should be statistically
indistinguishable to those who did. Therefore, we can safely treat
the missing values in the dependent variables as missing com-
pletely at random and use the sub-sample for dependent variables
in all analyses subsequent to multiple imputation.
Measures
The measurement for the outcome variable, Internet pornog-
raphy use, was collected by the GSS through two steps. It first
asks,‘‘in the past 30 days how often have you visited a website
for.’’ Then, with a list of different types of websites, one can
choose from‘‘never, 1–2 times, 3–5 times, above 5 times’’ under
the ‘‘sexually explicit website’’ category. People that chose
‘‘never’’ are dichotomized as 0 while all others as 1. Earlier
studies used the GSS to study Internet pornography also adopted
this measurement (Stack et al., 2004; Wright & Randall, 2012).
A dichotomous question used to compare traditional pornog-
raphy with Internet pornography came from a GSS question
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asking whether the respondent has seen an X-rated movie in the
last year. This question was adopted by some scholars to study
pornography use in its generic form (Wright, 2011).
Social status is a more complex noti on. We should follow con -
temporary sociologists’ operation of social class as an aggre-
gate dimension based on achievement, power, and ownership
(Grusky, 2007; Petev, 2013). Five variables were used to repre-
sent social status: education and income, which are shown in many
cases as the most salient expression of high status (Grusky, 2007); a
subjective class identity (including lower cl ass , worki ng c lass ,
middle class, and upper class) which depictshow people per-
ceive themselves in a social ladder in comparison to others, it is
an important notion forclass consciousness (Jackman, 1979);
an objective social class variable crea ted by a combi nation of
GSS questions regarding ownership and labor-capital rela-
tionship, which complements the subjectivity in the self-de-
scribed class identity: retired or in school, the unemployed,
laborers, managers, self-employed artisans, or bourgeoisie
(Wright, 1980); another variable‘‘weeks wor ked la st yea r’’ refle cts
the labor autonomy and laboriousness that confound total
income (Petev, 2013;Veblen,1899/2005).
Social bonds refer to the attachment and commitment
to conventional institutions and beliefs (Hirschi, 1969).
The measurement for social bonds in this study included
religious attendance, fundamentalism (self-identified as funda-
mental versus moderate and liberal), attitude to porn law (di-
chotomized as ‘‘there should be law against pornography dis-
tribution whatever the age’’versus otherwise), and a seven-point
scale for liberalism–conservatism (1 designates extremely lib-
eral, 7 designates extremely conservative).
Three GSS questions measured sexual intercourse oppor-
tunities: marriage status, frequency of sex, and the number of
sex partners. The GSS asked the respondents’ marital status on
all surveys, and ‘‘how often did you have sex during the last
12 months’’ and‘‘how many sex partners have you had in the
last 12 months’’since 1988.This study recodedmarital status to
‘‘never married, currently married, divorce/widow/separated’’
and the number of sex partners to‘‘zero, one, two and more,’’
while keeps sex frequency asa continuous variable.
There are three variables measuring the opportunity to access
the Internet: (1) the presence of teenagers at home; (2) a five-
point Likert scale of internet knowledge created by summing
three GSS variables: the ability to download a file, transfer a file,
and understand computer virus; (3) whether the respondent pos-
sesses a computer. The presence of teenager used as an oppor-
tunity factor is advised by previous studies on pornography
that argued children may serve as‘‘whistle blower’’to deter the
adult from watching pornography (Akers & Sellers, 2000), or
compete for the time to use computer (Stack et al., 2004).
Demographic controlswere selected with the advice of a meta-
analysis on Internet pornography use (Paul, 2009),including race,
age, gender, rural residence, and the U.S. geography areas.
Statistical Analyses
Before performing the main analyses, multiple imputationwas
conducted to handle the missing data by creating five additional
samples for a completed dataset based on the chained multiple
imputation method, which is preferred in large sample with
missing values across several variables of different types (Azur,
Stuart, Frangakis, & Leaf, 2011). For imputing binary or ordinal
variable with less than five levels, the link function assumed the
form of logit; for interval variables, the link function was pre-
dictive mean matching, a similar method to regression except
that it takes donor values from the closest predicted value for the
missing ones. The‘‘mi estimate’’ applied the weighting and com-
bination rules to analyze the imputed full samples in all subsequent
models that would be otherwise biased due to sample inflation.
This method allows the estimation of parameters as the average of
coefficients from the imputed datasets, and calculates standard
errors based on the degree to which the coefficient estimates
vary across the imputations (Rubin, 1996; UCLA: Statistical
Consulting Group, 2006). While the‘‘mi estimate’’ command
results in the final estimates of the entire imputed dataset, it
does not show information from each imputed dataset. To
calculate McFadden’s Pseudo-R-squared based on the same
Rubin’s combinationrule and obtain other informationsuch as
residualsand predicted probabilities,the‘‘mi xeq’’commandin
Stata was also executed when such information is desired.
Sample characteristics were described for each variable by
mean or proportion, and the range of values. Logistic regres-
sion was used to estimate the association between the inde-
pendent and dependent variables reporting odds ratio as effect
size and the 95% confidence intervals for the odds ratios; a p
value of 0.05 was to determine the statistical significance of
each coefficient reported. To testwhether highersocial status is
related to higher sexual release opportunities, multinomial logistic
regression and OLS (ordinary least squares) models with depen-
dent variables (marriage/sex frequency/number of sex partner)
regressing on the social status variables were conducted with
the control variables. The next step introduced three models to
test: (1) the baseline association of social status and Internet
pornography when only controlling for demographic back-
ground; (2) the association after introducing social bonds and
the Internet access opportunities; and (3) to finalize the asso-
ciation after introducing sexual intercourse opportunities. If
social status influences Internet pornography use th rough sexual
intercourse opportunities, the coefficients for social status vari-
ables would drop in the final step (a partial mediation) or cease to
be significant (full mediation) (Yang, Kelly, & Yang, 2014). All
models included the same set of control variables to compare the
effects meaningfully.
The same set of models also compared traditional porno-
graphic consumption using another dependent variable‘‘watch-
ing X-rated movie’’for the rationale stated in the introduction part
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of this study. All models had the demographic controls consis-
tently present. When each model yields a McFadden’s R
2
,
Harel’s transformation of Rto Zwas manually computed—
where Zis the inverse tangent of R—as a method to adjust for
the central tendency (Harel, 2009).
Results
Before testing the relationships between the data, I have pre-
sented descriptive statistics in Table 1to help understand the
sample characteristics. Out of all respondents recruited between
2000 and 2005 for the survey section containing Internet pornog-
raphy use, 12.3 % reported having watched Internet pornography
during the past 30 days, but only 3.8 % claimed‘‘there should be no
laws prohibiting pornography.’’ Twenty-four percent of respon-
dents who answered the question on X-rated movie reported having
watched it in the past year. For sexual intercourse opportunities,
almost half of all people were married at the time of the surveys.
More than 64.6 % reported a sex partner, 21.0 % reported no sex
partners, while the rest have more than one sex partners. The
mean value on the scale of sex frequency falls approximately on
the level of ‘‘2 or 3 times a month’’; its one standard deviation
from the average would be‘‘once or twice a year’’ or‘‘2 or 3 times
a week.’’ The demographic distribution of races, sex, rural resi-
dency, and region mostly affirms the representativeness of this
sample to the U.S. National Census. Education, average age, and
racial composition correspond to the Census figures. The family
income of the GSS sample averages around $30,000, and its
median category of $35,000–$40,000 is close to but lower than
the 2004 National Census median of $44,000 (DeNavas-Walt,
Proctor, & Lee, 2005). Working length averages at 34.3 weeks/
year, but the deviation is high. If only counting the ever employed,
the average working weeks will be 46.7 weeks (not shown here)
and approximates the 44 weeks in National Census that used the
same criterion (U.S. Census Bureau, 2005).
In order to test the hypothesis that Internet pornography use is
triggered by the lack of sexual intercourse opportunities, regres-
sion models in Table 2estimate the associations between social
status and sexual intercourse opportunities. The analyses of three
independent regression models on sexual intercourse opportu-
nity variables in Table 2shows most indicators of higher social
status point to more sexual intercourse opportunities, with an excep-
tion of education. Each additional year of education is associated
with higher likelihood of never being married, having no sex part-
ner, and less frequent sexual activities. Higher income is signifi-
cantly (p\.0001 for all cases) associated with higher probability of
being married or having a sex partner. One unit increase in income
level corresponds to a change of odds ratio to 0.77 for being never
married as opposed to being married. A person is 14 % less likely to
have no sex partner when income level increases by one, and the
increase of sex frequency is 0.08 for each income level, net of the
effects of all other variables. Even within the same (i.e., controlling
for) social class and income and education level, longer working
length leads to higher likelihood of being never married (OR =
1.01, p\.0001) and divorce/widow/separated (OR =1.02, p\
.0001). Note that although the odds ratio for working length seems
small, the additive effect can be strong because the scale of working
length is large as 52 units. The subjective class identity does not
significantly influence sexual intercourse opportunities, save
that the working class are less likely to be never married. For
objective social class, when artisan/bourgeoisie is the refer-
ence group, the unemployed people are almost twice as likely
to be never married as opposed to currently married, and 1.51
times more likely to have no sex partner. The labors are 1.54
times more likely to have no sex partner, or their sexual activ-
ity is less frequent. All the associations have controlled for
demographic background and social bonds.
Table 3displays the association coefficients and other
statistics of the three logistic regression models predicting
Internet pornography use. The baseline model regresses Internet
pornography use on social status while controlling for the
impact of demographic backgrounds at the same time, to
ensure the association between Internet pornography use and
social status clearly exists even if controlling for racial, age,
gender, and residential differences. For one unit increase of
income out of the 23 total levels, the odd of using Internet
pornography is down by a factor of 0.94 (p\.001). In the
same manner, the model estimates that each additional year of
education reduces the odds ratio by a factor of 0.95 (p\.05).
Working weeks and subjective class identity are not signifi-
cantly associated with Internet pornography use, but the odds
ratio of using Internet pornography among retired or at school
persons was only 0.50 (p\.05) when compared with the bour-
geoisieclass. As fordemographiccontrols, age and genderprove
to be two significant factors, with male far more likely to report
consuming Internet pornography than female. This baseline
model explains a total of 16 % of the dependent variable’s
variance, according to both McFadden’s pseudo R
2
and Harel’s
Ztransformation.
The next model adds three Internet use opportunity variables
and four social bonding variables. This model tests whether the
opportunity to use the Internet and technological competency
will bias against some potential pornography users with less
internet accessibility. It also evaluates the influences of bonding to
traditional beliefs and institutes on watching Internet pornog-
raphy. Model 2 in Table 3shows that knowledge about the I nt ern et
is not associated with online pornography use in a statistically
significant manner, neither among computer-possessors nor
among non-possessors. However, people reported having
teenager at home are only 0.54 times as likely (p\.05) to use
pornography online in last 30 days. Among the indicators of social
bonds, one higher category of religious attendance decreases the
likelihood of reporting watching Internet pornography by a factor of
0.54 (p\.05). Social status variables remain similar to the previous
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Table 1 Descriptive characteristics of the sample
Percentage (%) Mean (SD) Range
Used internet pornography last 30 days 12.34 0,1
Watched X-rated movie in last year 24.5 0,1
Income level 15.9 (5.45) 1–23
Education (years) 13.44 (2.92) 0–20
Working weeks per year 34.3 (22.33) 0–52
Subjective class identity 0,1
Lower class 5.99
Working class 44.61
Middle class 44.97
Upper class 4.43
Objective class location 0,1
Labor 41.9
Unemployed 5.6
Retire or at school 30.9
Manager 13.3
Artisan/Bourgeoisie 8.33
Internet knowledge 1.24 (0.39) 0–2
Computer possession 66.4 0,1
Presence of teenagers at home 0.17 (0.48) 0–7
Marriage 0,1
Married 47.97
Widow/divorce/separated 27.74
Never married 24.29
Number of sex partners 0,1
Zero 20.98
One 64.63
Two and more 14.39
Sex frequency 2.87 (1.98) 0–6
Attitude to porn law 3.76 0,1
Religious attendance 3.66 (2.72) 0–8
Fundamentalism 30.2 0,1
Liberalism–Conservatism 4.13 (1.41) 1–7
Race 0,1
White 79.04
Black 14.49
Others 6.47
Age 46.09 (17.18) 18–89
Male 44.52 0,1
Rural resident 11.22 0,1
Region 0,1
New England 4.36
Mid Atlantic 14.62
North Central 24.63
South Atlantic 19.14
South Central 17.15
Mountain 6.55
Pacific 13.53
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model, with an addition that longer working length is now associ-
ated with using Internet pornography (OR =1.02, p\.05). In
this model, social status remains significantly related to
Internet pornography use in the hypothesized direction even
when controlling for all other factors.
The third model of Table 3finally introduces the sexual inter-
course opportunity measures that seen in Table 2as outcome
variables. This model is the final model of particular interest
because it estimates whether social status still constitutes a sig-
nificant factor of Internet pornography use and whether its
impact goes via sexual intercourse opportunities. Here, a never
marriedpersonis1.6times(p\.05) more likely to watch Internet
pornography in the last 30 days than his/her married coun-
terpart, but having two and more sex partners is associated
with higher Internet pornography use by a factor of 1.92 (p\
.01). Sex frequency is not a significant factor after controlling
for marital status, the number of sex partners, and demo-
graphic backgrounds. Social status variables remain signifi-
cant in this model: people with more income (OR =0.95,
p\.05), higher education (OR=0.92, p\.05), and shorter
workinglength(OR=1.02, p\.01) have lower likelihood to use
Internet pornography, even when controlling for sexual inter-
course opportunities. The odds ratio for income drops from 0.93
to 0.95 from model 2, and the significant adrops from 0.01 to
Table 2 Multiple regression models on sexual intercourse opportunities with multiple imputation (n=5215, m=5)
Dependent variables Marriage status (base =married) Number of sex partners (base =1) Frequency of sex
Divorce/widow/separated Never married 0 2 and more
Social status
Income .76*** .77*** .86*** .92*** 0.08***
Education 1.5* 1.1*** 1.07** – -0.05***
Working weeks 1.02*** 1.01*** – – –
Subjective class (base =high class) – – – –
Lower class
Working class .59*
Middle class
Objective class (base =bourgeoisie) –
Labor 1.54* -0.20*
Unemployed 1.84** 1.51

Retired or at school .61*
Manager
Internet use opportunity
Internet knowledge*computer possession – – –
Possession-no – 1.93*
Possession-yes 1.58* 1.79*
Presence of teenager at home – .64*** .72*** – 0.21***
Social bonds
Religious attendance .88*** .88*** 1.08** .88*** -0.06***
Attitude to porn law – – – – –
Fundamentalism – 1.25* – – –
Liberalism–Conservatism 1.1* .87*** – .88** –
Demographics
Race (base =White)
Black 1.31* 2.11*** .57*** 1.68*** 0.36***
Others .65* – .54** – 0.21*
Age 1.03*** .93*** 1.04*** .94*** -0.04***
Male (=1) .72*** 1.2* – 2.02*** 0.23***
Rural residency (=1) .53*** .54*** .57** – 0.32***
Regions – – – – –
*p\.05, ** p\.01, *** p\.0001,

p\.06 (two-tailed test), omission indicates non-significance; coefficients for marriage status and number of sex
partners are odds ratios from logistic regressions, for sex frequency are unstandardized coefficients from an OLS
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0.05, indicating a partial mediation on income by sexual inter-
course opportunities. Note that although a 0.02 drop of odds ratio
may appear minimal, the scale of income is large enough to make
anoticeable difference: in model2,the highest earningpersonare
only0.18(=0.92
23
) as likely to watch Internet porn; after sexual
intercourse opportunities enter the model, this likelihood is
Table 3 Logistic regression models on internet porn use, with multiple imputation (n=1727, m=5)
Model 1: social status Model 2: social status, internet use
opportunity, social bonds
Model 3: full model including
sexual intercourse
OR 95 % CI OR 95 % CI OR 95 % CI
Social status
Income 0.94*** 0.91, 0.97 0.93** 0.89, 0.97 0.95* 0.91, 0.99
Education 0.95* 0.89, 0.99 0.92* 0.86, 0.99 0.92* 0.85, 0.98
Working weeks 1.00 0.99, 1.02 1.02* 1.00, 1.04 1.02** 1.01, 1.04
Subjective class (.=high class)
Lower class 1.46 0.49, 4.34 1.81 0.49, 6.75 2.12 0.46, 7.97
Working class 0.93 0.41, 2.15 1.02 0.40, 2.59 1.08 0.42, 2.80
Middle class 0.98 0.44, 2.18 1.03 0.42, 2.53 1.11 0.44, 2.81
Objective class (.=bourgeoisie)
Labor 0.78 0.51, 1.22 0.71 0.48, 1.15 0.69 0.42, 1.12
Unemployed 1.01 0.49, 2.07 0.80 0.34, 1.88 0.75 0.31, 1.79
Retired or at school 0.50* 0.26, 0.96 0.46 0.19, 1.07 0.41

0.17, 1.01
Manager 0.79 0.47, 1.34 0.69 0.39, 1.23 0.66 0.37, 1.17
Internet use opportunity
Internet knowledge*computer possession ns ns
No possession
Possession
Presence of teenagers at home 0.54* 0.34, 0.87 0.59* 0.37, 0.95
Sexual intercourse
Marriage (.=married)
Widow/divorce/separated 1.46 0.89, 2.39
Never married 1.60* 1.01, 2.54
Number of sex partner (.=1)
0 0.77 0.39, 1.51
2 and more 1.92** 1.24, 2.95
Sex frequency 0.93 0.83, 1.05
Attitude to porn law 1.17 0.39, 3.54 1.14 0.38, 3.41
Religious attendance 0.54* 0.34, 0.87 0.89*** 0.81, 0.96
Fundamentalism 1.39 0.97, 2.00 1.39 0.97, 2.01
Liberalism–Conservatism 0.93 0.80, 1.07 0.96 0.82, 1.12
Race (.=white)
Black 0.63 0.37, 1.05 0.57* 0.33, 0.98 0.76 0.42, 1.40
Others 0.94 0.57, 1.55 0.99 0.60, 1.66 1.19 0.67, 2.12
Age 0.97*** 0.96, 0.99 0.98*** 0.97, 0.99 0.98* 0.97, 1.00
Male (=1) 7.99*** 5.63, 11.3 8.23*** 5.75, 11.8 7.88*** 5.22, 11.88
Rural residency ns ns ns
Regions ns ns ns
R
2
: 0.16
Harel Z: 0.16
RVI: 0.0002
R
2
: 0.20
Harel Z: 0.20
RVI: 0.06
R
2
: 0.23
Harel Z: 0.22
RVI: 0.06
Harel’s Zis the sum of inverse tangent of R
2
divided by the times of imputation. RVI (relative variance inflation) shows the average proportion of variance
possibly inflated due to missing data
ns non-significance
*p\.05, ** p\.01, *** p\.0001,

p\.06 (two-tailed test)
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alleviated to 0.31 (=0.95
23
). McFadden’s R
2
andHarel’s Zsuggest
the final model explains 22–23 % of the total variances; the RVI
estimates that missing data addressed by the multiple imputation
has inflated 6 % of the variances, which is a relatively low risk
concern. Figure 1summarizes the associations between the pro-
posed concepts. For the sake of minimizing visual burden, it
shows only two categories for marital status (never married vs.
married) and the number of sex partners (0 vs. 1).
When comparing Internet pornography with traditional
media (in this case watching an X-rated movie), the model
designed to explain Internet pornography does not perform as
well. Table 4displays this information. Social status is not related
to watching X-rated movie in the last year, and of course neither
are Internet use opportunities. Among the sexual intercourse
opportunities, watching X-rated movie is only associated with
having two and more sex partners (OR =1.84, p\.01).
Althoughone should note thatthe coefficients inTables 3and 4
are not outwardly comparable due to different sample sizes and
variable timespans, they nevertheless provide a contrast showing
different pathways leading to X-rated movie watching and
Internet pornography watching.
Discussion
This study has tried to advance our understanding of Internet
pornography use among adults in the U.S. during the early
2000s. Earlier works on Internet pornography mostly aimed at
clinical utility or individual characteristics, while this study
particularly investigates whether lower social status may lead
to more Internet pornography use, and whether such associ-
ation is caused by the lack of sexual intercourse opportunities
among the lower status individuals.
The first highlight is that lower social status does lead to the
lack of sexual intercourse opportunities by all three measures,
even after controlling for social bonds and demographic back-
grounds. People who earn less income and work longer weeks
are less likely to be currently married or have a sex partner, and
their frequency of sex are lower too. Compared with artisan/
bourgeoisie, who occupy an independent/dominant labor-capi-
tal relationship, laborers are more likely to have no sex partners
and have less frequent sex. The unemployed also tend to be
never married and have no sex partners. The only exception to
the study’s hypotheses is education, which is negatively asso-
ciated with all sexual intercourse measures. It is well known that
higher education has the impact of delaying initial marriage, and
this may be the reason why education as a social status indicator
is negatively related to sexual intercourse opportunities.
Thethirdsetofmodelsindicatepeople with lower socialsta-
tus again displayed higher odds ratio of using Internet pornog-
raphy, even after controlling for social bonds, opportunities to
access the Internet, and demographic background. People who
earn less income, work longer weeks, and received less educa-
tionshoweda higherlikelihoodofusingInternetpornography.
With an exception of income, the associations between social
status variables and Internet pornography use were robust even
after sexual intercourse opportunities were entered as mediators,
suggesting these social status variables independently influence
Internetpornography use. Incomeindeed was partiallymediated
by marital status, echoingthe hypothesis that people with more
income are less likely to watch Internet pornography, partly
because they already have the sexual intercourse opportunity
brought by being married. However, having more than one sex part-
ner is strongly associated with watching Internet pornography. If
watching Internet pornography is only a means of compensating
the lack of sexual intercourse opportunities as anticipated by the
Fig. 1 Relationships between social status, sexual intercourse oppor-
tunities, and internet pornography use. Odds ratios of social status on
sexual release opportunities are from Table 2, effects of social status and
sexual intercourse opportunities on internet pornography use are from
Table 3, and all models are controlled for the same set of variables. Odds
ratios of social status ordered by income, education, weeks worked,
subjective class identity, and objective class; never married is compared
against currently married, and zero sex partner is compared against one
partner. *\.05, **\.01, ***\.001. Dotted line indicates coefficient not
significant at a=.05
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hypothesis and also partly supported by the mediation of marital
status on income, people with more sex partners would be less
likely to watch Internet pornography (or X-rated movies as well).
The answer to this dilemma extends beyond the aim of this
study, but potential explanations exist. It is possible that only
stable and committed relationships such as marriage provide
Table 4 Logistic regression models on X-movie watch, with multiple imputation (n=2320, m=5)
Model 1: social status Model 2: social status, internet
use opportunity
Model 3: full model including sexual
intercourse
OR 95% CI OR 95 % CI OR 95 % CI
Social status
Income ns ns ns
Education ns ns ns
Working weeks ns ns ns
Subjective class (.=high class) ns ns ns
Lower class
Working class
Middle class
Objective class (.=bourgeoisie) ns ns ns
Labor
Unemployed
Retired or at school
Manager
Internet use opportunity
Internet knowledge*computer possession ns ns
No possession
Possession
Presence of teenagers at home ns ns
Sexual intercourse
Marriage (.=married)
Widow/divorce/separated 1.32

0.98, 1.78
Never married 1.14 0.84, 1.53
Number of sex partner (.=1)
0 0.77 0.48, 1.22
2 and more 1.84** 1.38, 2.45
Sex frequency 0.93 0.83, 1.05
Attitude to porn law ns ns
Religious attendance 0.84*** 0.80, 0.89 0.86*** 0.81, 0.91
Fundamentalism ns ns
Liberalism–Conservatism 0.87*** 0.81, 0.94 0.88** 0.81, 0.95
Race (.=white)
Black 1.16 0.89, 1.51 1.37* 1.01, 1.88 1.23 0.95, 1.69
Others 0.98 0.71, 1,35 1.08 0.74, 1.58 1.05 0.72, 1.54
Age 0.95*** 0.95, 0.96 0.95*** 0.95, 0.96 0.96*** 0.95, 0.97
Male (=1) 2.91*** 2.43, 3.48 2.91*** 2.34, 3.59 2.77*** 2.23, 3.44
Rural residency ns ns ns
Regions ns ns ns
R
2
: 0.13
Harel Z: 0.13
RVI: 0.0005
R
2
: 0.14
Harel Z: 0.14
RVI: 0.03
R
2
: 0.16
Harel Z: 0.16
RVI: 0.04
Harel’s Zis the sum of inverse tangent of R
2
divided by the times of imputation. RVI (relative variance inflation) shows the average proportion of variance
possibly inflated due to missing data
ns non-significance
*p\.05, ** p\.01, *** p\.0001,

p\.06 (two-tailed test)
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stable and consistent means of sexual intercourse. Some studies
have found that better sexual satisfaction is associated with
stable relationships, which in contemporary society still mostly
refer to long-term monogamous pairing (Costa & Brody, 2012;
Sprecher, 1998,2002). Another plausible explanation lies in the
subcultural characteristics circulated among certain Internet
pornography users, for whom consuming pornography is a nat-
ural extension of a specific set of offline behaviors (Peter &
Valkenburg, 2007). Therefore, the ‘‘rich get richer’’ Matthew
effect exists for people who already have the sexual release but
are nevertheless encouraged by a sexualized subculture to pur-
sue richer experiences.
Third, when comparing Internet pornography use with watch-
ing X-rated movie, this study has found that social status affects
watching Internet pornography, but not the traditional medium of
X-rated movies. The explanation lies in line with our proposed
hypothesis: people with lower social status may utilize Internet
pornography because it is easily accessible, cheaper, and anon y-
mous, which came from the narrowing digital gap. Tradi-
tional erotic media do no offer these advantages, thus may
further interfere with social status and render the statistical
associations noisy. Therefore, this study suggests the distinct
relationship between social status and Internet pornography
indeed comes from the features pertain to online behaviors.
Lastly, during the coming of the 21st century, many have real-
ized the phenomenon of an ubiquitous use of the Internet in the
developed world, but others argue that social and cultural posi-
tions will still determine how people use the Internet and wh at c on -
tent they utilize thereof (Graham, 2008; Peter & Valkenburg,
2006). In this context, if one decides to watch Internet
pornography, internet knowledge and possession are not the
primary concern, but such decision itself rests upon external
social factors. This studyindicates that internet knowledgeand
computer possession do not determine Internet pornography
use, but social status does. Having teenagers at home is still
negatively related to Internet pornography use, confirming the
opportunity theory that contends teenagers can act as surveil-
lance and‘‘whistle blowers’’ (Akers & Sellers, 2000). Among
all social bonds, religious attendance is negatively associated
with Internet pornography use, but the attitude to pornography
law and political views failed to exert significant influence.
Limitations
Albeit the merits discussed above, this study admits a few lim-
itations caused by survey design, sampling, and the choice of
measurement. First, due to the rotation design of the GSS, certain
variables of immense interest are simply unavailable for the
survey period between 2000 and 2004, when Internet pornog-
raphyusewasasked.Suchvariablesincluding ‘‘viewing X-rated
movie via theatre and VCR’’ could have more accurately cap-
tured the nature of traditional pornographic materials. The time
frame for comparing Internet pornography use and traditional
pornographic media is not identical because the measurement
for Internet pornography counts at monthly unit while that for X-
movie measures at yearly unit, thus making their comparison
less straightforward.
Second, there are a few more candidates for sexual inter-
course opportunity measurement, including ‘‘had pick-up sex
last year,’’‘‘sex with a neighbor last year,’’and‘‘sex with a friend
last year.’’ Using these variables may yield certain interesting
results. However, the current study has not included these
variables because they were only asked after‘‘the number of sex
partner’’ question. The number of sex partner can sufficiently
measure the variance, and it has less missing data for easier
multiple imputation.
Third, readers should bear in mind that this study explores the
Internet pornography use during the first half of the 2000s. The
access to the Internet is quickly expanding in the United States, it
might have reached a point since 2004 where technological
opportunity to watch pornography no longer consists of having a
computer and understanding internet knowledge, which are the
indicators in this study, but more about which online platform
users adopt to search and circulate information. Newer cohorts
tend to distance themselves intentionally from older cohorts’
online behaviors (Lenhart, Purcell, Smith, & Zickuhr, 2010). In
the similar manner that younger generation do not share news by
sending emails, the newer cohort of netizens may use Internet
pornography for cultural distinctions as well. Future research
should definitely explore this untrodden area.
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