income inequality not gender inequality positively ... · 8/21/2018 · income inequality figure...

www.pnas.org/cgi/doi/10.1073/pnas. 115

Running head: FEMALE SEXUALIZATION ON SOCIAL MEDIA 1

Income Inequality Not Gender Inequality Positively Covaries With Female

Sexualization On Social Media

Supplementary Information Appendix

Authors: Khandis R. Blakea*, Brock Bastianb, Thomas F. Densonc, Pauline Grosjeana,d, Robert C.

Brooksa

Affiliations: aEvolution & Ecology Research Centre, and School of Biological, Earth and

Environmental Sciences, The University of New South Wales, Sydney NSW 2052, Australia.

bSchool of Psychological Sciences, University of Melbourne, Melbourne VIC 3006, Australia.

cSchool of Psychology, Mathews Building, The University of New South Wales, Sydney NSW

2052, Australia. dSchool of Economics, The University of New South Wales, Sydney NSW 2052.

*Correspondence to: Dr Khandis Blake, Evolution & Ecology Research Centre, School of

Biological, Earth and Environmental Sciences, The University of New South Wales, Sydney

NSW 2052, Australia. Email: [email protected]

1717959

mailto:[email protected]

FEMALE SEXUALIZATION ON SOCIAL MEDIA 2

For Supplementary Information, see pp. 3–21.

For Statistical Models, see pp. 22–163.


Figures

Figure S1 represents a non-parametric thin-plate spline generated using the Fields package

in R. Thin-plate splines allow the plotting of complex relationships between two independent

variables and a dependent variable to be assessed [48]. Unlike the parametric methods we used

for hypothesis testing, thin-plate splines do not constrain the kinds of relationship possible.

Instead, they apply a smoothing parameter arrived at by a resampling-based generalized cross-

validation (GCV) method, allowing the detection of complex response-surface features if there is

good statistical support within the data set for these features. The dependent variable was the

count of sexy selfies in each country, log-transformed, and the independent variables were z-score

standardized. Once each thin-plate spline was estimated, we plotted the predicted surface as heat

maps using the image and contour functions in the Graphics package version 3.2.1 of R.


Hum

an

de

ve

lop

me

nt

Income inequality

Figure S1. The relationship between income inequality, human development, and sexy

selfies across countries.

Note. The heat map is a non-parametric thin-plate spline depicting the prevalence of sexy selfies,

where red indicates more selfies. Predictors are z-scores and the dependent variable has been log-

transformed to facilitate interpretation.


Descriptive Statistics

Table S1. Post summary statistics for sexy selfie posts across geographic locations.

Location

US city US county Nation

N (geographies) 5,567 1,622 113

N (sexy selfie posts) 10,337 7,680 67,038

Posts per location range 0–668 posts 0–1,232 posts 1–19,361 posts

Posts per location M (SD) 1.86 (15.89) 4.73 (36.69) 593.26 (1,959.20)


Table S2. Pearson Correlation Table, City Level.

Note. ** p < .01. * p < .05. EduEmplEarn = component score of female education, female employment, and female median income.

Sexyselfies.

restricted

unique

Selfies GI.health GI.college

GI.

reproductive

GI.

managerial GI.income GI.factor Gini Top5percent 8020ratio EduEmplEarn MedianageF Sexratio

R .998**

.923**

-0.037 .060*

-0.023 -0.041 -.102**

-.081**

.127**

.128**

.112**

0.024 -.073**

-0.047

N 1622 1622 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R .925**

-0.034 .056*

-0.022 -0.040 -.102**

-.080**

.125**

.126**

.110**

0.019 -.071**

-0.045

N 1622 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -0.028 .072**

-0.027 -.050*

-.120**

-.092**

.153**

.154**

.142**

0.028 -.094**

-.059*

N 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -.113**

.192**

0.042 .210**

.382**

.117**

.057*

.087**

-.356**

-.113**

-.061*

N 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -.118**

.096**

.121**

.379**

-.170**

-.052*

-.130**

.317**

-.162**

.417**

N 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -0.024 -0.015 .127**

.071**

0.036 0.008 -.301**

-0.014 .091**

N 1605 1605 1605 1605 1605 1605 1605 1605 1605

R .297**

.615**

-.129**

-0.022 -.159**

-0.039 -.051*

.053*

N 1622 1605 1622 1622 1622 1622 1622 1622

R .769**

-.251**

-.221**

-.204**

-.088**

-.111**

-0.004

N 1605 1622 1622 1622 1622 1622 1622

R -.204**

-.133**

-.196**

-.108**

-.125**

.226**

N 1605 1605 1605 1605 1605 1605

R .879**

.892**

-.321**

-.142**

-.230**

N 1622 1622 1622 1622 1622

R .681**

-.122**

-0.047 -.182**

N 1622 1622 1622 1622

R -.251**

-.288**

-.249**

N 1622 1622 1622

R .063*

-.093**

N 1622 1622

R .158**

N 1622

Top5percent

8020ratio

EduEmplEarn

MedianageF

GI.college

GI.reproductive

GI.managerial

GI.income

GI.factor

Gini

Sexyselfies

Sexyselfies.

restricted

uniqueSelfies

GI.health


Table S3. Correlation Table, County Level.


Sexyselfies.

restricted

unique

Selfies GI.health GI.college

GI.

reproductive

GI.

managerial GI.income GI.factor Gini Top5percent 8020ratio EduEmplEarn MedianageF Sexratio

R .998**

.923**

-0.037 .060*

-0.023 -0.041 -.102**

-.081**

.127**

.128**

.112**

0.024 -.073**

-0.047

N 1622 1622 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R .925**

-0.034 .056*

-0.022 -0.040 -.102**

-.080**

.125**

.126**

.110**

0.019 -.071**

-0.045

N 1622 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -0.028 .072**

-0.0274408 -.050*

-.120**

-.092**

.153**

.154**

.142**

0.028 -.094**

-.059*

N 1622 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -.113**

.192**

0.042 .210**

.382**

.117**

.057*

.087**

-.356**

-.113**

-.061*

N 1622 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -.118**

.096**

.121**

.379**

-.170**

-.052*

-.130**

.317**

-.162**

.417**

N 1605 1622 1622 1605 1622 1622 1622 1622 1622 1622

R -0.024 -0.015 .127**

.071**

0.036 0.008 -.301**

-0.014 .091**

N 1605 1605 1605 1605 1605 1605 1605 1605 1605

R .297**

.615**

-.129**

-0.022 -.159**

-0.039 -.051*

.053*

N 1622 1605 1622 1622 1622 1622 1622 1622

R .769**

-.251**

-.221**

-.204**

-.088**

-.111**

-0.004

N 1605 1622 1622 1622 1622 1622 1622

R -.204**

-.133**

-.196**

-.108**

-.125**

.226**

N 1605 1605 1605 1605 1605 1605

R .879**

.892**

-.321**

-.142**

-.230**

N 1622 1622 1622 1622 1622

R .681**

-.122**

-0.047 -.182**

N 1622 1622 1622 1622

R -.251**

-.288**

-.249**

N 1622 1622 1622

R .063*

-.093**

N 1622 1622

R .158**

N 1622

Top5percent

8020ratio

EduEmplEarn

MedianageF

GI.college

GI.reproductive

GI.managerial

GI.income

GI.factor

Gini

Sexyselfies

Sexyselfies. restricted

uniqueSelfies

GI.health


Table S4. Correlation Table, National Level.

Note. ** p < .01. * p < .05.

sexySelfies.

restricted

unique

Selfies

sexySelfies

instagram

Development

factor

Gender

inequality

component

Gini

R 1.000**

.990**

.826**

.213*

-0.072 0.043

N 113 113 113 111 110 113

R 1 .991**

.833**

.212*

-0.071 0.042

N 113 113 113 111 110 113

R .991**

1 .851**

.226*

-0.090 0.017

N 113 113 113 111 110 113

R .833**

.851**

1 .246**

-0.098 -0.019

N 113 113 113 111 110 113

R .212*

.226*

.246**

1 -.679**

-.356**

N 111 111 111 111 109 111

R -0.071 -0.090 -0.098 -.679**

1 .406**

N 110 110 110 109 110 110

Development factor

Gender inequality

component

sexySelfies

sexySelfies.restricted

uniqueSelfies

sexySelfies instagram


Table S5. Correlation Table, Beauty dataset.


GI.health GI.college

GI.

reproductive GI.income

GI.

managerial Gini EduEmplEarn medianageF sexratio

R -0.004 0.033 0.007 -.069**

-0.005 .165**

-0.015 -.065**

-0.02244

N 1980 1980 1950 1980 1980 1980 1980 1980 1979

R 0.040 .063**

.163**

.106**

-0.020 -0.040 -0.010 -.099**

N 1980 1950 1980 1980 1980 1980 1980 1979

R -.086**

.186**

.239**

-.062**

.222**

-0.024 .409**

N 1950 1980 1980 1980 1980 1980 1979

R -.129**

-.066**

0.025 -.298**

-.126**

-0.040

N 1950 1950 1950 1950 1950 1949

R .359**

0.024 .305**

0.041 -.071**

N 1980 1980 1980 1980 1979

R 0.033 .202**

0.017 .132**

N 1980 1980 1980 1979

R -.149**

.078**

-.104**

N 1980 1980 1979

R .324**

.047*

N 1980 1979

R .181**

N 1979

GI.income

GI.managerial

Gini

EduEmplEarn

medianageF

Beauty sales

GI.health

GI.college

GI.reproductive


Table S6. Correlation Table, Clothing dataset.


GI.health GI.college

GI.

reproductive GI.income

GI.

managerial Gini EduEmplEarn medianageF sexratio

R -0.006 0.018 -0.002 -0.054 -0.009 .112** -0.018 -0.030 -0.011

N 1297 1298 1271 1298 1297 1298 1298 1298 1297

R 0.049 0.043 .162** .113** -.057* -0.020 -0.037 -0.023

N 1297 1271 1297 1297 1297 1297 1297 1297

R -.191** .175** .325** -.089** .271** -0.051 .435**

N 1271 1298 1297 1298 1298 1298 1297

R -.155** -.087** .082** -.387** -.148** -.059*

N 1271 1271 1271 1271 1271 1271

R .394** .091** .282** 0.049 -.064*

N 1297 1298 1298 1298 1297

R .106** .241** .071* .145**

N 1297 1297 1297 1297

R -.116** .055* -.125**

N 1298 1298 1297

R .365** .080**

N 1298 1297

R .162**

N 1297

GI.income

GI.managerial

Gini

EduEmplEarn

medianageF

Clothing sales

GI.health

GI.college

GI.reproductive


Table S7. Raw Variable Descriptive Statistics

City County Nation Beauty Clothing

M SD M SD M SD M SD M SD

Gender oppression variables

GI (health insurance) 46.26% 7.61% 46.19% 3.44% a a 4.96% 7.07% 44.44% 6.39%

GI (college opportunity) 46.87% 4.01% 46.43% 2.38% a a 47.22% 4.19% 52.75% 3.37%

GI (reproductive health) 19.02 36.44 21.97 19.80 a a 19.87 35.56 21.77 31.87

GI (management occupations) 58.92% 11.40% 61.39% 5.75% a a 59.77% 10.22% 59.88% 10.07%

GI (income) 58.23% 7.46% 49.64% 3.00% a a 59.27% 4.93% 58.97% 5.28%

Income inequality variables

Gini coefficient 0.43 0.05 0.44 0.03 0.38 0.85 .43 .05 .45 .05

Top 5% Share 19.00 3.83 20.14 2.34 - - - - - -

80:20 ratio 13.12 9.45 13.50 3.53 - - - - - -

Confounders

Female education level 91.60% 7.32% 91.36% 4.57% a a 92.41% 5.61% 91.74% 6.29%

Female median age 39.17 6.89 40.52 4.83 a a 39.57 6.33 39.06 7.01

Female median income $29.3K $9.3K $23.3K $4.6K a a $31.1K $9.1K $30.0K $9.3K

Female employment rate 91.9% 4.32% 40.52% 4.82% a a 92.54% 3.41% 92.14% 3.56%

Operational sex ratio 52.68% 6.65% 54.03% 4.01% a a 52.38% 6.68% 52.09% 5.45%

Urbanization 96.97% 8.64% 58.73% 25.17% a a 97.93% 8.63% 97.78% 9.07%

Note. GI = gender inequality. - = data was not available. a = represented using a standardized component score.


Table S8. Sexy Selfie Model Fit Statistics

Model Nnull Nfinal AICnull AICfinal ΔAIC BICnull BICfinal ΔBIC LLnull LLfinal ΔLL

City Model 1 5400 5400 8594.4 8504.7 -89.7 8607.6 8590.4 -17.2 -4295.2 -4239.4 55.8***

City Model 2 5513 5513 8382.2 8222.4 -159.8 8395.4 8255.5 -139.9 -4189.1 -4106.2 82.9***

City Model 3 5398 5398 8584.9 8437.2 -147.7 8598.1 8536.1 -62.0 -4290.5 -4203.6 86.8***

City Model 4 5375 5375 8593.8 8383.8 -210.0 8607.0 8535.4 -71.6 -4294.9 -4168.9 126.0***

County Model 1 1588 1588 4391.3 4061.8 -329.5 4402.1 4120.9 -281.2 -2193.7 -2019.9 173.8***

County Model 2 1601 1601 4404.3 3989.9 -414.4 4415.1 4011.4 -403.7 -2200.2 -1990.9 209.2***

County Model 3 1587 1587 4393.9 4012.0 -381.9 4404.6 4076.4 -328.2 -2194.0 -1994.0 201.0***

County Model 4 1535 1535 4116.6 3868.4 -248.2 4128.2 3975.1 -152.1 -2056.3 -1914.2 142.1***

Nation Model 1 108 108 1417.3 1417.0 -0.3 1425.4 1427.8 2.4 -705.7 -704.5 1.15ns

Nation Model 2 110 110 1438.0 1426.1 -12.0 1446.1 1436.9 -9.3 -716.0 -709.0 7.0***

Nation Model 3 108 108 1417.3 1414.9 -2.4 1425.4 1428.3 2.9 -705.7 -702.4 3.2***

Nation Model 4 107 107 1403.7 1397.6 -6.1 1411.7 1416.3 4.6 -698.9 -691.8 7.0**

Note. ***p < .001, ** p < .01, * p < .05.


Sexy Selfie Method

Sexy Selfie Search Terms

The sexy and hot words were: sexy, hot, sexydress, hotgirls, sohot, sexygirls, sosexy,

hotchicks, sexybabe, sexyladies, sexypic, hotbabe, hotty, sexylegs, sexys, hotness, sexytime,

sexybody, hotass, hotwoman, sexyaf, supersexy, hotashell, superhot, sexyashell, hotaf, sexybitch,

sexylady, sexyass, sexyasfuck, and hotlegs. The selfie terms were: selfie, selfshot, mirrorselfie,

mirrorshot, sexyselfie, and hotselfie.

Geolocation Matching

For city level analyses, we matched strings in the user’s location field to all US cities,

villages, boroughs, towns, and census designated places (5,575 city locations) with populations

estimated to exceed 5,000 people from the 2015 five year ACS population estimates (the only

estimates available when we started analyzing the data) [32]. First, we manually identified

locations in the US which shared the same name. We then appended the less populated location

with the relevant state or state abbreviation. For example, Arlington Texas (population 291,255)

was matched to the term ‘Arlington’, whereas Arlington Virginia (population 188,663) was

matched to the terms ‘Arlington VA’ and ‘Arlington Virginia’. Where duplicate locations names

were in the same state, we appended the least populated location with the location type set by the

census (i.e., city, borough, town, or village; otherwise these terms were excluded). For six

duplicate location pairs which shared a state and census location type, we retained only the

location with the largest population. We also excluded two census designated places named

‘University, Florida’ due to general ambiguity, leaving n = 5,567 city locations.

We then cross-checked each location in this list against the names of (a) all UN

recognized countries [49], (b) all cities worldwide with populations exceeding 100K [50], and (c)

all counties in the United Kingdom and Canada, again appending cities which shared their name

with a county, country, or worldwide city (with a larger population) with their state and state


abbreviation. Using macros in Visual Basic, we again match strings in the user’s location field

with identical strings in the location list described above. All location strings that matched to

multiple locations (e.g., the location string “NYC|Washington DC|London” matched to three

cities) were resolved manually by deletion unless it was clear that only one location was relevant

(e.g., “I live in Washington DC | Go the NYC Nicks!”). The macro matched 10,337 posts to a

city, village, borough, census designated place, or town in the USA (range = 0–668 posts, M =

1.86, SD = 15.89). A random sample of 100 matches showed that 96% of city level locations were

correctly matched.

We followed this same procedure to match posts to counties in the US. We downloaded a

list of all US counties with populations greater than 20,000 people (N = 1,622) [32] and appended

duplicate county names with their state name and abbreviation (with the least populated county

being appended). We also searched and appended duplicates that matched UK and Canada

counties, other countries, or other cities with populations exceeding 100K (as in city level

matching). We then aggregated all city posts into their respective counties [51], discarding 2,610

posts from 111 cities which were under the jurisdiction of multiple counties. This procedure

matched 7,643 posts from city locations into their respective counties. We then re-searched user

location field strings for county matches, geolocating an additional 37 posts. The number of posts

across the 1,622 counties ranged from 0–1,232 posts (M = 4.74, SD = 36.69).

To match posts to nations worldwide, we utilized a list of all countries recognized by the

UN (N = 193 countries) in English, French, Spanish, Italian, and German languages [52]. Added

to this list were the names of all cities worldwide with populations exceeding 100K [50] (N =

3,712 cities) and all counties, provinces, states, territories, regions, or districts of the 10 countries

with the greatest percentage of Twitter users [53]. We again resolved duplicate names by

appending the city with the smallest population with the country name then the country ISO

abbreviation. We excluded worldwide locations with less than four characters due to the


likelihood of inaccurate matches (N = 29 cities). We then matched user location field strings to

identical strings in this list, aggregating matches to the nation level. This procedure matched

68,496 posts to 182 countries worldwide. A random sample of 100 matches showed that 99% of

user locations were correctly identified.

Although we matched location fields to country names in non-English languages, we

tracked sexy selfie search terms in English only, meaning that countries which did not post

frequently in English were under-represented. To account for this bias, we measured the

frequency of English language posts on Twitter in a 30 second window every 30 minutes for 24-

hours (N = 44,492 tweets). We determined the national location of these posts following the same

procedure outlined above, then aggregated matches to determine the frequency of English tweets

for each nation. Seventy of the 193 UN-recognized countries had zero English posts and were

excluded from further analyses; we also excluded 10 countries for whom a Gini coefficient could

not be computed due to missing data, leaving n = 113 countries (posts ranged from 1–19,361

posts, M = 593.26, SD = 1959.20). Thirty-eight percent (38.1%) of these countries were classified

by the UN as Very High Human Development Countries; 30.1% were High Human Development

Countries; 20.4% were Medium Human Development Countries, and 11.5% were Low Human

Development Countries.

Sexy Selfie Measures

Except where stated otherwise, city and county level measures were collected from the

2016 US Census Bureau five year estimates from the American Community Survey (ACS) [32].

Nation level income inequality was collected from the UN [26] or The World Bank [54] (with

preference given to the source which yielded the most recent estimate). Where income inequality

estimates from the UN and The World Bank were unavailable, we consulted the Central

Intelligence Agency World Factbook [55]. The Gini coefficient was our measure of income

inequality. At the city and county level, the measure was computed based on households. At the


nation level, coefficients were based on households or individuals (data from the UN is based on

both) or households only (data from the CIA and Worldbank). We operationalized gender inequity

in US city and US counties as follows. For the reproductive health dimension, we measured the

percent of people without health insurance who were women and the adolescent fertility rate

(births per 1000 15–19 year old women). For the empowerment dimension, we measured the

percent of people who had achieved some college education who were men and the percent of

people in management occupations who were men. The percent of women holding parliamentary

seats was not applicable for city and county level analyses and we chose to measure education at

the college level rather than secondary level to avoid ceiling effects. For the labor dimension, we

measured the percent of the combined male and female median income attributed to men. For all

inequality measures, higher scores reflected more gender inequality.

The education variable reflected the percentage of women who had achieved more than a

year 10 education and the age variable was the median female age. The income variable measured

the median earnings for the female civilian population aged 16 years and over. The employment

variable was the percent of women aged 16 years and over who were employed. We entered all

variables except age into a principal components analysis for city, county, beauty salon, and

clothing store data. The KMO statistics and Bartlett’s test of sphericity indicated that the data

were adequate for component analysis in all cases (KMOs > .60; χ2(3) > 640.25, ps <.001),

extracting one component which accounted for >60% of the variance. We calculated the

operational sex ratio by determining the percent of unmarried 18–44 year old people who were

male. Urbanization reflected the percentage of the local area that was urban (the most recent

estimates were from the 2000 US Census Bureau ACS).

Human development & English-language posting frequency component analysis

The 113 nations used in our analyses varied widely on their level of human development.

Because less developed nations have poorer access to telecommunication technologies and access


to the Internet is associated with gendered preferences [56], we controlled for differences in

human development between countries. We measured gross domestic product per capita, median

age, life expectancy, urbanization, population, access to the internet, and the Human

Development Index (HDI) at the nation level. The HDI is a composite index measuring average

achievement in three basic dimensions of human development—a long and healthy life,

knowledge, and a decent standard of living [26]. We entered these variables and the percent of

English-language Twitter posts into a principal components analysis with varimax rotation. The

KMO statistic and Bartlett’s test of sphericity indicated that the data were adequate for component

analysis (KMO = .86; χ2(28) = 726.40, p <.001) and the analysis extracted two components which

accounted for 75.88% of the variance.

Component 1 (59.43%) comprised the HDI, gross domestic product per capita, median

age, life expectancy, urbanization, and internet accessibility and thus appeared to represent

general human development. Component 2 (15.00%) comprised population and percent of

English posts and thus appeared to represent social media posting volume. Variable loadings

ranged from .77–.97 on the respective components and from -.14 to .17 on the alternate

component. We saved component scores using the regression method, controlled for Component

1 scores in all nation analyses, and offset all nation models by Component 2 scores to account for

social media volume effects on sexy selfie frequency.

Cross-national gender inequality component analysis

We measured cross-national gender inequality via the physical insecurity, inequality in

family law/practice, and government framework for gender inequality subscales from the

WomanStats Database [33]. The physical security scale examines laws and practices amongst

nations regarding domestic violence, rape and sexual assault, marital or family rape, military and

war-related rape, and honor killings/femicide. The scale is ordinal and coding is explained in

Table S9. The inequity in family law/practice scale captures how inequitably family law is


conceptualized according to gender. It encompasses laws and practices regarding marital rape,

age at first marriage, whether women must consent to marry, presence of polygyny, abortion law,

attitudes and laws regarding gendered aspects of divorce, and women’s property rights. The scale

is ordinal and coding is explained in Table S10. The Government Framework for Gender Equality

Scale utilizes three subscales that identify a general framework for feminist action: (1) the legal

declaration of gender equality, (2) the existence of a gender equality action plan, and (3) a

commitment to the international gender equality framework of the Convention on the Elimination

of all Forms of Discrimination Against Women. These three subscales together produce a scale

ranging from 0–7, capture legislative, practical, and international determinants of feminist

government policies. The scale legend is in Table S11.

We entered the three scales into a principal components analysis with varimax rotation.

The KMO statistic and Bartlett’s test of sphericity indicated that the data were adequate for

component analysis (KMO = .61; Bartlett’s χ2(3) = 95.33, p <.001) and the analysis extracted one

component which accounted for 67.68% of the variance. Variable loadings ranged from .74–.90

and we saved the standardized component score using the regression method. This component

was strongly correlated with the Gender Inequality Index (GII) from the UN, r(107) = .78, p

< .001, which we did not include due to high collinearity with the human development

component, r(107) = -.86, p < .001, VIF = 4.43. Nevertheless, our results replicate using the UN

GII.


Table S9. Physical Security of Women Scale

Scale Scale legend

0 There are laws against domestic violence, rape, and marital rape; these laws are

enforced; there are no taboos or norms against reporting these crimes, which are

rare. There are no honor killings or femicides.a


generally enforced; there are taboos or norms against reporting these crimes (or

ignorance that these are reportable crimes), which crimes are not common. Honor

killings and femicides do not occur.


sporadically enforced; there are taboos or norms against reporting these crimes (or

ignorance that these are reportable crimes), which are common. Honor killings

and/or femicides are quite rare, occurring only in small pockets of the population,

and are condemned by society.

3 There are laws against domestic violence, rape, but not necessarily marital rape;

these laws are rarely enforced; there are taboos or norms against reporting these

crimes (or ignorance that these are reportable crimes), which affect a majority of

women. Honor killings and/or femicides may occur among certain segments of

society but are not generally accepted within the society.

4 There are no or weak laws b against domestic violence, rape, and marital rape, and

these laws are not generally enforced. Honor killings and/or femicides may occur

and are either ignored or generally accepted.

Note. a Femicide refers to the targeting killing of women (practices that sanction murder where

the overwhelming proportion of victims are female; e.g., witchcraft killings). b An example of a

weak law is the need for four male witnesses to prove rape occurred.

Table S10. Inequity in Family Law/Practice between Men and Women Scale

Scale Scale legend

0 Legal age of marriage is at least 18, and most (80%+) marry over that age.

Marriages younger than 16 are virtually unheard of. Polygyny is illegal and

extremely rare. Women are free to choose their spouse. Women know their rights

to consent and divorce and are free to exercise those rights without fear of

reprisal. Marital rape is illegal and actively prosecuted. Women and men have

equal rights to divorce. Woman can inherit property upon the death of a parent or

upon divorce. Abortion is safe and legal and not imposed by the state on women

(i.e. forced abortions are not an issue).

1 Legal age of marriage is 16 or higher and most (80%+) marry over age 16.

Polygyny is illegal and uncommon. Women are free to choose their spouse.

Women know their rights to consent and divorce and are free to exercise those

rights without fear of reprisal. Marital rape is illegal. Women and men have equal

rights to divorce. Woman can inherit property, but laws tend to favor men in

property rights, including asset division after divorce. Abortion is legal (although

may not be available on demand for the asking).

2 Legal age of marriage is 16 or higher, but girls marrying younger are common (up

to 25%). There is often an age difference between the legal age of marriage for

men and women, such that girls are allowed to marry at younger ages than males.


Polygyny is legal but unusual (<5% of women). Girls may not have full rights to

choose their spouse. Women may or may not know their rights to consent and

divorce. Marital rape may be illegal, but is not prosecuted and practice often

allows it. Generally speaking, the grounds for divorce for men and women are the

same, although there may be exceptions (i.e., exempting infidelity on the part of

the male, or infertility on the part of the female). Divorce laws systematically

favor men, and women do not have equal rights in child custody matters.

Abortions may be restricted, but there are many reasons for permission to be

given, including financial reasons.

3 Legal age of marriage is 15 or lower, but girls marrying younger are common

(between 25-50%). Age discrepancies in the average age of men and women

getting married is often greater than 7 years or more, with women often averaging

less than 15 years old at time of marriage. Polygyny is legal and not uncommon

(>5% but less than 25% of women). Girls often cannot chose their spouse.

Although obstacles exist that force women to meet a higher standard of

justification than men, women can seek divorce but are generally unaware of that

right. Women in certain areas of in certain ethnic or religious groups may either

be unaware of their rights to consent in marriage and to divorce, or may fear

reprisals if they exercise those rights; such rights may be very limited. Marital

rape is not acknowledged in law. Divorce laws systematically favor men, and

women do not have equal rights in child custody matters, or in inheritance law.

Abortions are severely restricted to cases where the life of the mother is at risk,

possibly also rape and incest.

4 Legal age of marriage does not exist or allows girls younger than 12 to marry.

Girls commonly (more than 25%) marry around the age of 12 or even before

puberty. Women are rarely asked for consent before marriage, and women are

often forced to marry much older men in this way. Polygyny is legal and common

(>25%). Women must overcome tremendous legal obstacles to sue for divorce,

while men can seek divorce for many reasons. Women may be unaware of their

right to give consent in marriage or to divorce their husbands, may not legally

possess such rights, or may feel that the exercise of those rights would bring dire

physical or social consequences. Women are not awarded custody or inheritance.

Marital rape is not illegal. Abortions are illegal (you may also take cases where

states impose abortions on women, i.e., forced/coerced abortions).

Table S11. Government Framework for Gender Equality Scale

Scale Scale legend

0–1 Strong policies across all three dimensions (law, action plans, CEDAW)

2–3 Strong policies exist on most, but not all, dimensions

4–5 Gender equality policies may exist, but are inadequate on more than one dimension

6–7 No or very weak policies on gender equality across all three dimensions


Gender Matching Procedure

Though the majority of users had female names, we did not exclude users with male

names (or users where gender was indeterminable). To approximate gender for all posts, we

manually checked 1,500 randomly selected posts and manually identified user gender and

whether they posted photos mainly of themselves or of others. This check identified that 62% of

users were women and 71% of users shared photos mainly of themselves. We then used the R

package GenderizeR [57] to approximate user gender for each post in the dataset. GenderizeR

uses a dataset of 216,286 distinct names across 79 countries and 89 languages to predict gender

from first names extracted from text corpuses. We controlled for the accuracy of prediction by

setting the counts of first names in the genderize.io names database to ≥100 and by setting the

probability level to >0.60. This process ensured that only frequently cited names with a greater

probability of being one gender or another were included.

To determine whether posts were selfies or posts of the other gender, we analyzed the

frequency of gender-related keywords in the post text (to determine the gender of the subject

photographed in the post). We operationalized references to women via the inclusion of the words

"girl", "lady", "woman", "chick", "ladies", "babe", "female", "women", "babe", "she", "wife",

"mother", " mom", "milf", and " her". The equivalent male words were "male", " boy", " man",

"men", gay", "he ", "husband", "father", " dad", "guy", and "his". We split all posts by user gender

to determine people posting selfies or people posting photos of the other gender. We found that

when men were posting sexy selfies, 87% of them were posting (or re-posting) selfies of women

and not of themselves (reposts were more common, comprising 54% of men’s posts). By contrast,

90% of women were posting selfies of women (presumably themselves) and not of men. In total,

just over three quarters of all posts entailed women posting genuine selfies, and men (and very

occasionally, women) reposting them.


Model Appendix Table of Contents

SUMMARY OF DATA ANALYSIS STRATEGY ............................................................ 27

MODELS REPORTED IN MANUSCRIPT .................................................................................. 28

City Models ........................................................................................................................ 28

City Model 1 .......................................................................................................... 28

City Model 2 .......................................................................................................... 29

City Model 3 .......................................................................................................... 30

City Model 4 .......................................................................................................... 31

County Models ................................................................................................................... 32

County Model 1 ..................................................................................................... 32

County Model 2 ..................................................................................................... 33

County Model 3 ..................................................................................................... 34

County Model 4 ..................................................................................................... 35

Nation Models .................................................................................................................... 36

Nation Model 1 ...................................................................................................... 36

Nation Model 2 ...................................................................................................... 37

Nation Model 3 ...................................................................................................... 38

Nation Model 4 ...................................................................................................... 39

Beauty Models ................................................................................................................... 40

Beauty Model 1 ...................................................................................................... 40

Beauty Model 2 ...................................................................................................... 41

Beauty Model 3 ...................................................................................................... 42

Beauty Model 4 ...................................................................................................... 43

Clothing Models................................................................................................................. 44

Clothing Model 1 ................................................................................................... 44

Clothing Model 2 ................................................................................................... 45

Clothing Model 3 ................................................................................................... 46

Clothing Model 4 ................................................................................................... 47

ROBUSTNESS CHECKS ............................................................................................................. 48

Models with Alternative Measures of Gender Inequality .................................................. 48

City Models with Single GI Predictors .................................................................. 48

City Model 3 with GI.health ...................................................................... 49

City Model 4 with GI.health ...................................................................... 50

City Model 3 with GI.reproductive ............................................................ 51

City Model 4 with GI.reproductive ............................................................ 52

City Model 3 with GI.college .................................................................... 53


City Model 4 with GI.college .................................................................... 54

City Model 3 with GI.managerial .............................................................. 55

City Model 4 with GI.managerial .............................................................. 56

City Model 3 with GI.income .................................................................... 57

City Model 4 with GI.income .................................................................... 58

County Models with Single GI Predictors ............................................................. 59

County Model 3 with GI.health ................................................................. 59

County Model 4 with GI.health ................................................................. 60

County Model 3 with GI.reproductive ....................................................... 61

County Model 4 with GI.reproductive ....................................................... 62

County Model 3 with GI.college ............................................................... 63

County Model 4 with GI.college ............................................................... 64

County Model 3 with GI.managerial ......................................................... 65

County Model 4 with GI.managerial ......................................................... 66

County Model 3 with GI.income ............................................................... 67

County Model 4 with GI.income ............................................................... 68

Beauty Models with Single GI Predictors ............................................................. 69

Beauty Model 3 with GI.health .................................................................. 69

Beauty Model 4 with GI.health .................................................................. 70

Beauty Model 3 with GI.reproductive ....................................................... 71

Beauty Model 4 with GI.reproductive ....................................................... 72

Beauty Model 3 with GI.college ................................................................ 73

Beauty Model 4 with GI.college ................................................................ 74

Beauty Model 3 with GI.managerial .......................................................... 75

Beauty Model 4 with GI.managerial .......................................................... 76

Beauty Model 3 with GI.income ................................................................ 77

Beauty Model 4 with GI.income ................................................................ 78

Clothing Models with Single GI Predictors ........................................................... 79

Clothing Model 3 with GI.health ............................................................... 79

Clothing Model 4 with GI.health ............................................................... 80

Clothing Model 3 with GI.reproductive ..................................................... 81

Clothing Model 4 with GI.reproductive ..................................................... 82

Clothing Model 3 with GI.college ............................................................. 83

Clothing Model 4 with GI.college ............................................................. 84

Clothing Model 3 with GI.managerial ....................................................... 85

Clothing Model 4 with GI.managerial ....................................................... 86

Clothing Model 3 with GI.income ............................................................. 87


Clothing Model 4 with GI.income ............................................................. 88

Models with GI Factor ........................................................................................... 89

City Model 3 with GI Factor ...................................................................... 90

City Model 4 with GI Factor ...................................................................... 91

County Model 3 with GI Factor ................................................................. 92

County Model 4 with GI Factor ................................................................. 93

Beauty Model 3 with GI Factor ................................................................. 94

Beauty Model 4 with GI Factor ................................................................. 95

Clothing Model 3 with GI Factor ............................................................... 96

Clothing Model 4 with GI Factor ............................................................... 97

Models with Alternate Measures of income Inequality ..................................................... 98

Top 5 Percent Models ............................................................................................ 99

City Model 2 Top 5 Percent ....................................................................... 99

City Model 3 Top 5 Percent ..................................................................... 100

City Model 4 Top 5 Percent ..................................................................... 101

County Model 2 Top 5 Percent ................................................................ 102



80:20 Ratio Models .............................................................................................. 105

City Model 2 80:20 Ratio ........................................................................ 105

City Model 3 80:20 Ratio ........................................................................ 106

City Model 4 80:20 Ratio ........................................................................ 107

County Model 2 80:20 Ratio .................................................................... 108

County Model 3 80:20 Ratio .................................................................... 109

County Model 4 80:20 Ratio ..................................................................... 110

Models with Additional Pornography Exclusions ............................................................ 111

Models Excluding Pornography Keywords .......................................................... 111

City Model 1 with Additional Pornography Exclusions ........................... 112




County Model 1 with Additional Pornography Exclusions ...................... 116




Nation Model 2 with Additional Pornography Exclusions ...................... 121




Instagram-Only Models ....................................................................................... 124

Nation Model 1 with Instagram-Only ...................................................... 125




Models with Unique Users Only...................................................................................... 127

City Model 1 with Unique Users Only ................................................................ 128




County Model 1 with Unique Users Only ........................................................... 132




Nation Model 1 with Unique Users Only ............................................................ 136




Models with Posts from Women of Women .................................................................... 140

City Model 1 from Women of Women ................................................................. 141




County Model 1 from Women of Women ............................................................ 145




Nation Model 1 from Women of Women ............................................................. 149




Models with Restrictions on City Size............................................................................. 153

City Model 1 with Cities>20K ............................................................................. 154





Models with Urbanization then Income Inequality ......................................................... 158

City Model with Urbanization then Income Inequality ....................................... 159

County Model with Urbanization then Income Inequality .................................. 160

Models with only non-WEIRD countries ........................................................................ 161

Model 1 with only non-WEIRD countries ........................................................... 162





SUMMARY OF DATA ANALYSIS STRATEGY

Data analyses were conducted in R using both Windows and Linux operating systems and

the packages glmmADMB, DescTools, AER, and lme4. We evaluated each model iteratively as

soon as it is modified, to quantify and qualify the fit improvement [58]. Below is our approach for

every count model. We followed the same approach for linear models (but with a gaussian

distribution), excluding Steps 1 and 2.

1. We tested the suitability of poisson versus negative binomial distributions, retaining the

model with the smallest AIC fit. All poisson models were significantly over-dispersed and

none were used.

2. We then compared negative binomial models against zero-inflated negative binomial

models using Vuong’s test and a comparison of AIC values, to see if excess zeros warranted

a dual-approach analytic strategy. All negative binomial models provided superior fits.

3. We added a random intercept and retained it when its inclusion warranted a small decrease

in AIC units (approximately Δ2-AIC units). We then added random slopes for all fixed

effects, again retaining them when their inclusion warranted a small decrease in AIC units.

4. We excluded standardized Pearson Residuals > ± 2.96, to account for model outliers. For

the vast majority of models, no more than 2% of cases were excluded as outliers. Where

more than 2% outliers were excluded, we note this below the model output.

5. We compared this final model against the model with no predictors (the null model) using a

formalized likelihood ratio test.

6. We evaluated collinearity using Variance Inflation Factors. No predictor yielded VIFs

greater than 2.0, and most were below 1.50.


MODELS REPORTED IN MANUSCRIPT

City Models

City Model 1

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city1.rirs$residuals, center = TRUE) <= 2.96 & scale(city1.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, cityPopulation, cityState))), family = "nbinom") AIC: 8504.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6062 0.0908 -116.87 <2e-16 *** zcityGI.health -0.0737 0.0603 -1.22 0.2220 zcityGI.reproductive 0.0480 0.0682 0.70 0.4812 zcityGI.college 0.1013 0.0645 1.57 0.1162 zcityGI.managerial 0.0430 0.0690 0.62 0.5332 zcityGI.income -0.1482 0.0533 -2.78 0.0055 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5400, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 2.450e-01 0.495015 zcityGI.health 1.858e-02 0.136323 zcityGI.reproductive 4.344e-02 0.208415 zcityGI.college 3.691e-02 0.192122 zcityGI.managerial 5.792e-02 0.240676 zcityGI.income 8.636e-05 0.009293 Negative binomial dispersion parameter: 0.17488 (std. err.: 0.0087095) Log-likelihood: -4239.36


City Model 2

Call: glmmadmb(formula = citySexyselfies ~ zcityGini + (zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2.rirs$residuals, center = TRUE) <= 2.96 & scale(city2.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 8222.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6493 0.1011 -105.30 < 2e-16 *** zcityGini 0.3111 0.0525 5.92 3.1e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5513, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3624 0.6020 zcityGini 0.0271 0.1646 Negative binomial dispersion parameter: 0.20191 (std. err.: 0.01049) Log-likelihood: -4106.21


City Model 3

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3.rirs$residuals, center = TRUE) <= 2.96 & scale(city3.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 8437.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6893 0.0937 -114.11 < 2e-16 *** zcityGI.health -0.0858 0.0602 -1.43 0.154 zcityGI.reproductive 0.0431 0.0704 0.61 0.540 zcityGI.college 0.1010 0.0681 1.48 0.138 zcityGI.managerial 0.0261 0.0693 0.38 0.707 zcityGI.income -0.1171 0.0546 -2.15 0.032 * zcityGini 0.3261 0.0545 5.98 2.2e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5398, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.256050 0.50601 zcityGI.health 0.018856 0.13732 zcityGI.reproductive 0.052705 0.22958 zcityGI.college 0.048721 0.22073 zcityGI.managerial 0.059350 0.24362 zcityGI.income 0.001923 0.04385 zcityGini 0.026454 0.16265 Negative binomial dispersion parameter: 0.18753 (std. err.: 0.0097279) Log-likelihood: -4203.62


City Model 4

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4.rirs3$residuals, center = TRUE) <= 2.96 & scale(city4.rirs3$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 8383.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.80583 0.09036 -119.59 < 2e-16 *** zcityGI.health -0.11594 0.05813 -1.99 0.0461 * zcityGI.reproductive -0.00797 0.07321 -0.11 0.9133 zcityGI.college 0.14885 0.07636 1.95 0.0513 . zcityGI.managerial 0.08703 0.06441 1.35 0.1766 zcityGI.income -0.12830 0.05700 -2.25 0.0244 * zcityGini 0.29475 0.05783 5.10 3.5e-07 *** cityEduEmplEarnpca -0.21855 0.08978 -2.43 0.0149 * zcityMedianagefemale -0.23062 0.07965 -2.90 0.0038 ** zcitySexratio -0.08937 0.06788 -1.32 0.1880 zcityUrbanization 0.21492 0.13503 1.59 0.1115 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5375, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.1948200 0.44138 zcityGI.health 0.0053810 0.07336 zcityGI.reproductive 0.0567470 0.23822 zcityGI.college 0.0354470 0.18827 zcityGI.managerial 0.0298030 0.17264 zcityGI.income 0.0022040 0.04695 zcityGini 0.0178370 0.13356 cityEduEmplEarnpca 0.0843640 0.29045 zcityMedianagefemale 0.0806890 0.28406 zcitySexratio 0.0009892 0.03145 zcityUrbanization 0.2867200 0.53546 Negative binomial dispersion parameter: 0.20765 (std. err.: 0.011011) Log-likelihood: -4168.91


County Models

County Model 1

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county1.rirs2$residuals, center = TRUE) <= 2.96 & scale(county1.rirs2$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, countyPopulation, countyState))), family = "nbinom") AIC: 4061.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5174 0.1222 -94.24 <2e-16 *** zcountyGI.health -0.1285 0.1573 -0.82 0.414 zcountyGI.reproductive -0.1959 0.1784 -1.10 0.272 zcountyGI.college 0.2902 0.1427 2.03 0.042 * zcountyGI.managerial -0.0296 0.1129 -0.26 0.793 zcountyGI.income -0.2413 0.0964 -2.50 0.012 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1588, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3075 0.5546 zcountyGI.health 0.4024 0.6343 zcountyGI.reproductive 0.3633 0.6027 zcountyGI.college 0.2734 0.5229 Negative binomial dispersion parameter: 0.36548 (std. err.: 0.026937) Log-likelihood: -2019.9


County Model 2

Call: glmmadmb(formula = countySexyselfies ~ zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2.ri$residuals, center = TRUE) <= 2.96 & scale(county2.ri$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 3989.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4242 0.1082 -105.61 < 2e-16 *** zcountyGini 0.4676 0.0618 7.57 3.7e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1601, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3617 0.6014 Negative binomial dispersion parameter: 0.35878 (std. err.: 0.025348) Log-likelihood: -1990.94


County Model 3

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3.rirs$residuals, center = TRUE) <= 2.96 & scale(county3.rirs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 4012 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5791 0.1208 -95.88 < 2e-16 *** zcountyGI.health -0.1494 0.1453 -1.03 0.304 zcountyGI.reproductive -0.1389 0.1774 -0.78 0.434 zcountyGI.college 0.3288 0.1424 2.31 0.021 * zcountyGI.managerial -0.0347 0.1105 -0.31 0.753 zcountyGI.income -0.0111 0.0991 -0.11 0.911 zcountyGini 0.4921 0.0665 7.40 1.4e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1587, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3090 0.5559 zcountyGI.health 0.2967 0.5447 zcountyGI.reproductive 0.3715 0.6095 zcountyGI.college 0.2737 0.5232 Negative binomial dispersion parameter: 0.40125 (std. err.: 0.030065) Log-likelihood: -1993.98


County Model 4

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county4.rirs2$residuals, center = TRUE) <= 2.96 & scale(county4.rirs2$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies,zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization))),family = "nbinom") AIC: 3868.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.25e+01 1.47e-01 -84.67 < 2e-16 *** zcountyGI.health -2.15e-01 1.50e-01 -1.43 0.1527 zcountyGI.reproductive -2.06e-01 2.28e-01 -0.90 0.3669 zcountyGI.college -1.46e-02 1.64e-01 -0.09 0.9289 zcountyGI.managerial 9.78e-03 1.23e-01 0.08 0.9367 zcountyGI.income -3.44e-04 1.10e-01 0.00 0.9975 zcountyGini 2.68e-01 8.22e-02 3.25 0.0011 ** countyEduEmplEarnpca -1.69e-01 1.13e-01 -1.49 0.1371 zcountyMedianagefemale -2.02e-01 1.02e-01 -1.98 0.0482 * zcountySexratio -4.77e-02 1.87e-01 -0.26 0.7985 zcountyUrbanization 9.51e-01 1.34e-01 7.11 1.1e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1535, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 1.466e-01 0.382923 zcountyGI.health 2.587e-01 0.508665 zcountyGI.reproductive 8.703e-01 0.932909 zcountyGI.college 1.018e-01 0.318998 countyEduEmplEarnpca 3.699e-06 0.001923 zcountyMedianagefemale 8.275e-02 0.287672 zcountySexratio 3.343e-01 0.578152 zcountyUrbanization 4.436e-02 0.210616 Negative binomial dispersion parameter: 0.44401 (std. err.: 0.03512) Log-likelihood: -1914.179


Nation Models

Nation Model 1

Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation1.fe$residuals, center = TRUE) <= 2.96 & scale(nation1.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, ZGINI, GIFac.womenstats, devfactor, engpopfactorln))), family = "nbinom") AIC: 1417 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.456 0.129 42.20 < 2e-16 *** devfactor 1.178 0.239 4.94 7.9e-07 *** GIFac.womenstats 0.414 0.226 1.83 0.067 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=108 Negative binomial dispersion parameter: 0.55589 (std. err.: 0.0639) Log-likelihood: -704.516


Nation Model 2

Call: glmmadmb(formula = sexySelfies ~ devfactor + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation2.fe$residuals, center = TRUE) <= 2.96 & scale(nation2.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1426.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.400 0.128 42.13 <2e-16 *** devfactor 0.879 0.152 5.77 8e-09 *** ZGINI 0.278 0.141 1.98 0.048 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=110 Negative binomial dispersion parameter: 0.55463 (std. err.: 0.06319) Log-likelihood: -709.033


Nation Model 3

Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation3.fe$residuals, center = TRUE) <= 2.96 & scale(nation3.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1414.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.427 0.128 42.55 < 2e-16 *** devfactor 1.206 0.238 5.08 3.8e-07 *** GIFac.womenstats 0.334 0.220 1.51 0.130 ZGINI 0.279 0.141 1.98 0.048 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=108 Negative binomial dispersion parameter: 0.5713 (std. err.: 0.065883) Log-likelihood: -702.449


Nation Model 4

Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + ZGINI + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation4.fe$residuals, center = TRUE) <= 2.96 & scale(nation4.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1397.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.4901 0.1704 32.22 < 2e-16 *** devfactor 1.2005 0.2337 5.14 2.8e-07 *** GIFac.womenstats 0.0553 0.2452 0.23 0.8214 ZGINI 0.5512 0.1771 3.11 0.0019 ** devfactor:ZGINI 0.5997 0.2288 2.62 0.0088 ** devfactor:GIFac.womenstats -0.1850 0.1853 -1.00 0.3181 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=107 Negative binomial dispersion parameter: 0.59908 (std. err.: 0.069805) Log-likelihood: -691.811


Beauty Models

Beauty Model 1

Call: lm(formula = Zbeautysales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t, data = na.omit(subset(beauty, scale(beauty1.fe$residuals, center = TRUE) <= 2.96 & scale(beauty1.fe$residuals, center = TRUE) >= -2.96, select = c(Zbeautysales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zpopulation.t, State)))) Residuals: Min 1Q Median 3Q Max -1.031 -0.221 -0.065 0.114 36.251 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.002253 0.021480 -0.105 0.91648 Zpopulation.t 0.354131 0.024949 14.194 < 2e-16 *** ZGI.health.t -0.012423 0.021803 -0.570 0.56889 ZGI.reproductive.t -0.072714 0.024788 -2.933 0.00339 ** ZGI.college.t -0.001112 0.022593 -0.049 0.96077 ZGI.managerial.t 0.010171 0.023453 0.434 0.66458 ZGI.income.t -0.005037 0.024360 -0.207 0.83621 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9465 on 1935 degrees of freedom Multiple R-squared: 0.1058, Adjusted R-squared: 0.103 F-statistic: 38.15 on 6 and 1935 DF, p-value: < 2.2e-16


Beauty Model 2

Call: lm(formula = Zbeautysales ~ Zpopulation.t + Zgini.t, data = na.omit(subset(beauty, scale(beauty2.fe$residuals, center = TRUE) <= 2.96 & scale(beauty2.fe$residuals, center = TRUE) >= -2.96, select = c(Zbeautysales, Zpopulation.t, Zgini.t, State)))) Residuals: Min 1Q Median 3Q Max -0.69926 -0.14378 -0.03841 0.07996 2.96045 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.042306 0.006363 -6.649 3.82e-11 *** Zpopulation.t 0.192920 0.006557 29.424 < 2e-16 *** Zgini.t 0.059572 0.006452 9.233 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2824 on 1967 degrees of freedom Multiple R-squared: 0.3474, Adjusted R-squared: 0.3467 F-statistic: 523.6 on 2 and 1967 DF, p-value: < 2.2e-16


Beauty Model 3

Call: lm(formula = Zbeautysales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t + Zgini.t, data = na.omit(subset(beauty, scale(beauty3.fe$residuals, center = TRUE) <= 2.96 & scale(beauty3.fe$residuals, center = TRUE) >= -2.96, select = c(Zbeautysales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zpopulation.t, Zgini.t, State)))) Residuals: Min 1Q Median 3Q Max -1.081 -0.226 -0.063 0.110 36.089 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.002438 0.021341 -0.114 0.90908 Zpopulation.t 0.332395 0.025148 13.218 < 2e-16 *** ZGI.health.t -0.008500 0.021676 -0.392 0.69499 ZGI.reproductive.t -0.071662 0.024629 -2.910 0.00366 ** ZGI.college.t 0.010417 0.022559 0.462 0.64429 ZGI.managerial.t 0.006505 0.023312 0.279 0.78024 ZGI.income.t -0.012327 0.024244 -0.508 0.61120 Zgini.t 0.111811 0.021808 5.127 3.24e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9404 on 1934 degrees of freedom Multiple R-squared: 0.1178, Adjusted R-squared: 0.1146 F-statistic: 36.88 on 7 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4

Call: lm(formula = Zbeautysales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t + Zgini.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, scale(beauty4.fe$residuals, center = TRUE) <= 2.96 & scale(beauty4.fe$residuals, center = TRUE) >= -2.96, select = c(Zbeautysales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zgini.t, Zpopulation.t, EduEmplEarnpca.t, Zmedianagefemale.t, Zsexratio.t, Zurbanization.t, State)))) Residuals: Min 1Q Median 3Q Max -1.044 -0.232 -0.068 0.121 36.054 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0030436 0.0213262 -0.143 0.8865 Zpopulation.t 0.3522201 0.0266324 13.225 < 2e-16 *** ZGI.health.t -0.0002238 0.0220434 -0.010 0.9919 ZGI.reproductive.t -0.0469647 0.0260954 -1.800 0.0721 . ZGI.college.t -0.0015329 0.0256254 -0.060 0.9523 ZGI.managerial.t 0.0015712 0.0236712 0.066 0.9471 ZGI.income.t -0.0180161 0.0253989 -0.709 0.4782 Zgini.t 0.1194966 0.0223903 5.337 1.06e-07 *** EduEmplEarnpca.t 0.2703970 0.1095888 2.467 0.0137 * Zmedianagefemale.t 0.0179684 0.0248485 0.723 0.4697 Zsexratio.t 0.0044458 0.0251782 0.177 0.8599 Zurbanization.t -0.0400590 0.0226252 -1.771 0.0768 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9387 on 1926 degrees of freedom Multiple R-squared: 0.1242, Adjusted R-squared: 0.1192 F-statistic: 24.83 on 11 and 1926 DF, p-value: < 2.2e-16


Clothing Models

Clothing Model 1

Call: lm(formula = Zclothingsales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t, data = na.omit(subset(clothing, scale(clothing1.fe$residuals, center = TRUE) <= 2.96 & scale(clothing1.fe$residuals, center = TRUE) >= -2.96, select = c(Zclothingsales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zpopulation.t, State)))) Residuals: Min 1Q Median 3Q Max -0.31425 -0.11503 -0.04649 0.04123 2.03761 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045803 0.006319 -7.249 7.40e-13 *** Zpopulation.t 0.127743 0.007621 16.762 < 2e-16 *** ZGI.health.t 0.004753 0.006467 0.735 0.463 ZGI.reproductive.t -0.036908 0.007555 -4.885 1.17e-06 *** ZGI.college.t 0.010796 0.006877 1.570 0.117 ZGI.managerial.t 0.002941 0.007211 0.408 0.683 ZGI.income.t 0.001386 0.007792 0.178 0.859 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2228 on 1237 degrees of freedom Multiple R-squared: 0.2044, Adjusted R-squared: 0.2005 F-statistic: 52.95 on 6 and 1237 DF, p-value: < 2.2e-16


Clothing Model 2

Call: lm(formula = Zclothingsales ~ Zpopulation.t + Zgini.t, data = na.omit(subset(clothing, scale(clothing2.fe$residuals, center = TRUE) <= 2.96 & scale(clothing2.fe$residuals, center = TRUE) >= -2.96, select = c(Zclothingsales, Zpopulation.t, Zgini.t, State)))) Residuals: Min 1Q Median 3Q Max -0.37288 -0.13197 -0.06239 0.04085 2.60761 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.035922 0.007329 -4.902 1.07e-06 *** Zpopulation.t 0.116892 0.007440 15.712 < 2e-16 *** Zgini.t 0.042933 0.007368 5.827 7.11e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2636 on 1291 degrees of freedom Multiple R-squared: 0.1871, Adjusted R-squared: 0.1858 F-statistic: 148.6 on 2 and 1291 DF, p-value: < 2.2e-16


Clothing Model 3

Call: lm(formula = Zclothingsales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t + Zgini.t, data = na.omit(subset(clothing, scale(lmclothing3.fe$residuals, center = TRUE) <= 2.96 & scale(lmclothing3.fe$residuals, center = TRUE) >= -2.96, select = c(Zclothingsales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zpopulation.t, Zgini.t, State)))) Residuals: Min 1Q Median 3Q Max -0.35216 -0.11507 -0.04481 0.04260 1.97016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.044706 0.006300 -7.096 2.16e-12 *** Zpopulation.t 0.124215 0.007639 16.261 < 2e-16 *** ZGI.health.t 0.008496 0.006467 1.314 0.1892 ZGI.reproductive.t -0.038901 0.007537 -5.162 2.85e-07 *** ZGI.college.t 0.016535 0.006925 2.388 0.0171 * ZGI.managerial.t -0.003084 0.007269 -0.424 0.6715 ZGI.income.t -0.003636 0.007794 -0.467 0.6409 Zgini.t 0.044219 0.006509 6.793 1.70e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.222 on 1235 degrees of freedom Multiple R-squared: 0.2301, Adjusted R-squared: 0.2257 F-statistic: 52.72 on 7 and 1235 DF, p-value: < 2.2e-16


Clothing Model 4

Call: lm(formula = Zclothingsales ~ Zpopulation.t + ZGI.health.t + ZGI.reproductive.t + ZGI.college.t + ZGI.managerial.t + ZGI.income.t + Zgini.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, scale(clothing4.fe$residuals, center = TRUE) <= 2.96 & scale(clothing4.fe$residuals, center = TRUE) >= -2.96, select = c(Zclothingsales, ZGI.health.t, ZGI.reproductive.t, ZGI.college.t, ZGI.managerial.t, ZGI.income.t, Zgini.t, Zpopulation.t, EduEmplEarnpca.t, Zmedianagefemale.t, Zsexratio.t, Zurbanization.t, State)))) Residuals: Min 1Q Median 3Q Max -0.36501 -0.11322 -0.04267 0.04341 1.96011 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.043879 0.006230 -7.043 3.13e-12 *** Zpopulation.t 0.136574 0.008102 16.856 < 2e-16 *** ZGI.health.t 0.009272 0.006419 1.444 0.14886 ZGI.reproductive.t -0.026010 0.008190 -3.176 0.00153 ** ZGI.college.t 0.021889 0.008091 2.705 0.00692 ** ZGI.managerial.t -0.005397 0.007272 -0.742 0.45816 ZGI.income.t -0.007053 0.007967 -0.885 0.37619 Zgini.t 0.044166 0.006547 6.746 2.33e-11 *** EduEmplEarnpca.t 0.082466 0.033257 2.480 0.01328 * Zmedianagefemale.t 0.024227 0.007553 3.208 0.00137 ** Zsexratio.t -0.010275 0.007569 -1.358 0.17486 Zurbanization.t -0.022165 0.007101 -3.121 0.00184 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2196 on 1232 degrees of freedom Multiple R-squared: 0.2527, Adjusted R-squared: 0.2461 F-statistic: 37.88 on 11 and 1232 DF, p-value: < 2.2e-16


ROBUSTNESS CHECKS

Models with Alternative Measures of Gender Inequality

City Models with Single GI Predictors

To ensure that the inclusion of five gender inequality measures did not reduce the

likelihood of gender inequality affecting the dependent variables, we re-ran Models 3 and 4 for

city, county, beauty salon, and clothing store data comparing each individual gender inequality

measure against income inequality in separate models. For Model 3, we regressed sexy selfies

onto the Gini coefficient and the gender inequality predictor, and for Model 4 we added all

confounders. Results indicated that across dependent variables and models, the largest and most

reliable predictor of self-sexualization was income inequality. For sexy selfies at the city and

county level, income inequality was a larger and more reliable predictor of sexy selfie posting

than all individual gender inequality predictors. For beauty salon and women’s clothing store

expenditures, income inequality had a larger and more reliable effect than all gender inequality

predictors (except in one model were coefficients were the same size, though standard errors

favored the Gini, page 81), though some gender inequality predictors were significant in both

models.


City Model 3 with GI.health

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGini + (zcityGI.health + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.health.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.health.m1rs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.health, cityState, cityPopulation))), family = "nbinom") AIC: 8603.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6620 0.0954 -111.78 < 2e-16 *** zcityGI.health -0.1410 0.0685 -2.06 0.04 * zcityGini 0.3405 0.0509 6.68 2.3e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5511, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.28969 0.5382 zcityGI.health 0.06506 0.2551 zcityGini 0.01788 0.1337 Negative binomial dispersion parameter: 0.18367 (std. err.: 0.0091119) Log-likelihood: -4294.91


City Model 4 with GI.health

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.health + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.health.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.health.m2rs2$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.health, cityState, cityPopulation, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization))), family = "nbinom") AIC: 8582.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7817 0.0912 -118.28 < 2e-16 *** zcityGI.health -0.1323 0.0594 -2.23 0.0259 * zcityGini 0.3183 0.0557 5.72 1.1e-08 *** cityEduEmplEarnpca -0.1987 0.0844 -2.36 0.0185 * zcityMedianagefemale -0.2533 0.0811 -3.12 0.0018 ** zcitySexratio 0.0181 0.0650 0.28 0.7809 zcityUrbanization 0.2065 0.1257 1.64 0.1006 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5484, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.21453 0.4632 zcityGI.health 0.01393 0.1180 zcityGini 0.01584 0.1259 cityEduEmplEarnpca 0.08623 0.2936 zcityMedianagefemale 0.10222 0.3197 zcitySexratio 0.02808 0.1676 zcityUrbanization 0.24043 0.4903 Negative binomial dispersion parameter: 0.20423 (std. err.: 0.010379) Log-likelihood: -4276.34


City Model 3 with GI.reproductive

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.reproductive + zcityGini + (zcityGI.reproductive + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.reproductive.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.reproductive.m1rs$residuals, center = TRUE) >= -2.96)), family = "nbinom") AIC: 7838 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.58743 0.09568 -110.65 < 2e-16 *** zcityGI.reproductive -0.00421 0.06822 -0.06 0.95 zcityGini 0.27445 0.05368 5.11 3.2e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=4936, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.29007 0.5386 zcityGI.reproductive 0.04047 0.2012 zcityGini 0.01882 0.1372 Negative binomial dispersion parameter: 0.17393 (std. err.: 0.00893) Log-likelihood: -3912

NB: Residual outliers accounted for 10% of the data. There was no substantial change to the effect sizes when residual outliers were included.


City Model 4 with GI.reproductive

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.reproductive + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.reproductive + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.reproductive.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.reproductive.m2rs2$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.reproductive, cityState, cityPopulation, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization))), family = "nbinom") AIC: 8342.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7904 0.0892 -120.97 < 2e-16 *** zcityGI.reproductive -0.0146 0.0698 -0.21 0.8346 zcityGini 0.3209 0.0569 5.64 1.7e-08 *** cityEduEmplEarnpca -0.1693 0.0912 -1.86 0.0633 . zcityMedianagefemale -0.2589 0.0821 -3.15 0.0016 ** zcitySexratio 0.0468 0.0650 0.72 0.4720 zcityUrbanization 0.2137 0.1314 1.63 0.1040 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5377, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.19421 0.4407 zcityGI.reproductive 0.04622 0.2150 zcityGini 0.01739 0.1319 cityEduEmplEarnpca 0.11810 0.3437 zcityMedianagefemale 0.10374 0.3221 zcitySexratio 0.03005 0.1733 zcityUrbanization 0.26586 0.5156 Negative binomial dispersion parameter: 0.19993 (std. err.: 0.010323) Log-likelihood: -4156.11


City Model 3 with GI.college

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.college + zcityGini + (zcityGI.college + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.college.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.college.m1rs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.college, cityState, cityPopulation))), family = "nbinom") AIC: 8201.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6710 0.1011 -105.55 < 2e-16 *** zcityGI.college 0.1207 0.0691 1.75 0.081 . zcityGini 0.3378 0.0517 6.54 6.3e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5513, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.35851 0.5988 zcityGI.college 0.07050 0.2655 zcityGini 0.02216 0.1489 Negative binomial dispersion parameter: 0.20868 (std. err.: 0.010949) Log-likelihood: -4093.75


City Model 4 with GI.college

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.college + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.college + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.college.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.college.m2rs2$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.college, cityState, cityPopulation, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization))), family = "nbinom") AIC: 8556.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7642 0.0910 -118.24 < 2e-16 *** zcityGI.college 0.1446 0.0722 2.00 0.0451 * zcityGini 0.3209 0.0559 5.74 9.6e-09 *** cityEduEmplEarnpca -0.2355 0.0832 -2.83 0.0046 ** zcityMedianagefemale -0.2179 0.0788 -2.77 0.0057 ** zcitySexratio -0.0254 0.0647 -0.39 0.6949 zcityUrbanization 0.2161 0.1291 1.67 0.0941 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5485, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.219150 0.46813 zcityGI.college 0.034001 0.18439 zcityGini 0.016268 0.12755 cityEduEmplEarnpca 0.074352 0.27268 zcityMedianagefemale 0.087111 0.29515 zcitySexratio 0.007918 0.08898 zcityUrbanization 0.245930 0.49591 Negative binomial dispersion parameter: 0.21065 (std. err.: 0.010768) Log-likelihood: -4263.44


City Model 3 with GI.managerial

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.managerial + zcityGini + (zcityGI.managerial + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.managerial.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.managerial.m1rs$residuals, center = TRUE) >= -2.96), select = c(citySexyselfies, zcityGini, zcityGI.managerial, cityState, cityPopulation)), family = "nbinom") AIC: 7770.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6179 0.0962 -110.40 < 2e-16 *** zcityGI.managerial -0.0175 0.0742 -0.24 0.81 zcityGini 0.2939 0.0548 5.36 8.2e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=4935, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.28922 0.5378 zcityGI.managerial 0.09371 0.3061 zcityGini 0.02101 0.1449 Negative binomial dispersion parameter: 0.17826 (std. err.: 0.0092723) Log-likelihood: -3878.36



City Model 4 with GI.managerial

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.managerial + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.managerial + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.managerial.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.managerial.m2rs2$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.managerial, cityState, cityPopulation, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization))), family = "nbinom") AIC: 8570.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7775 0.0906 -118.93 < 2e-16 *** zcityGI.managerial 0.0427 0.0594 0.72 0.473 zcityGini 0.3202 0.0557 5.75 9.1e-09 *** cityEduEmplEarnpca -0.1888 0.0842 -2.24 0.025 * zcityMedianagefemale -0.2660 0.0811 -3.28 0.001 ** zcitySexratio 0.0402 0.0649 0.62 0.536 zcityUrbanization 0.2025 0.1276 1.59 0.113 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5485, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.21328 0.4618 zcityGI.managerial 0.02439 0.1562 zcityGini 0.01592 0.1262 cityEduEmplEarnpca 0.07546 0.2747 zcityMedianagefemale 0.10040 0.3169 zcitySexratio 0.03293 0.1815 zcityUrbanization 0.24164 0.4916 Negative binomial dispersion parameter: 0.20376 (std. err.: 0.010406) Log-likelihood: -4270.33


City Model 3 with GI.income

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.income + zcityGini + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.income.m1$residuals, center = TRUE) <= 2.96 & scale(GI.income.m1$residuals, center = TRUE) >= -2.96), select = c(citySexyselfies, zcityGini, zcityGI.income, cityState, cityPopulation)), family = "nbinom") AIC: 7783.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.5773 0.1025 -103.18 <2e-16 *** zcityGI.income -0.1209 0.0493 -2.45 0.014 * zcityGini 0.2829 0.0459 6.17 7e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=4936, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.361 0.6009 Negative binomial dispersion parameter: 0.17505 (std. err.: 0.0088972) Log-likelihood: -3886.9



City Model 4 with GI.income

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.income.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.income.m2rs2$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, zcityGI.income, cityState, cityPopulation, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization))), family = "nbinom") AIC: 8559.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7796 0.0920 -117.22 < 2e-16 *** zcityGI.income -0.0885 0.0571 -1.55 0.121 zcityGini 0.3125 0.0576 5.42 5.9e-08 *** cityEduEmplEarnpca -0.1990 0.0826 -2.41 0.016 * zcityMedianagefemale -0.2486 0.0805 -3.09 0.002 ** zcitySexratio 0.0388 0.0637 0.61 0.542 zcityUrbanization 0.1891 0.1257 1.50 0.132 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5485, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.22569 0.4751 zcityGI.income 0.01892 0.1376 zcityGini 0.02211 0.1487 cityEduEmplEarnpca 0.08202 0.2864 zcityMedianagefemale 0.09962 0.3156 zcitySexratio 0.02983 0.1727 zcityUrbanization 0.24085 0.4908 Negative binomial dispersion parameter: 0.21264 (std. err.: 0.011017) Log-likelihood: -4264.66


County Models with Single GI Predictors

County Model 3 with GI.health

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGini + (zcountyGI.health + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.health.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.health.m1rs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGini, zcountyGI.health, countyState, countyPopulation))), family = "nbinom") AIC: 4019.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4775 0.1130 -101.55 < 2e-16 *** zcountyGI.health -0.1159 0.1396 -0.83 0.41 zcountyGini 0.4317 0.0819 5.27 1.3e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1604, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.34325 0.5859 zcountyGI.health 0.28487 0.5337 zcountyGini 0.08085 0.2843 Negative binomial dispersion parameter: 0.38173 (std. err.: 0.028296) Log-likelihood: -2002.83


County Model 4 with GI.health

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.health + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.health.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.health.m2rs2$residuals, center = TRUE) >= -2.96, select = c(select = c(countySexyselfies, zcountyGini, zcountyGI.health, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization, countyPopulation)))), family = "nbinom") AIC: 3874.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.3161 0.1588 -77.57 < 2e-16 *** zcountyGI.health -0.2285 0.1491 -1.53 0.12547 zcountyGini 0.2658 0.0787 3.38 0.00073 *** countyEduEmplEarnpca -0.2175 0.1051 -2.07 0.03844 * zcountyMedianagefemale -0.1593 0.1055 -1.51 0.13115 zcountySexratio -0.0904 0.1708 -0.53 0.59662 zcountyUrbanization 0.9445 0.1336 7.07 1.6e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1547, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 3.964e-01 0.629595 zcountyGI.health 2.390e-01 0.488835 zcountyGini 1.062e-05 0.003259 countyEduEmplEarnpca 2.649e-06 0.001628 zcountyMedianagefemale 1.066e-01 0.326451 zcountySexratio 3.393e-01 0.582538 zcountyUrbanization 7.046e-02 0.265437 Negative binomial dispersion parameter: 0.39908 (std. err.: 0.030301) Log-likelihood: -1922.28


County Model 3 with GI.reproductive

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.reproductive + zcountyGini + (zcountyGI.reproductive + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.reproductive.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.reproductive.m1rs$residuals, center = TRUE) >= -2.96)), family = "nbinom") AIC: 3962.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.3604 0.1159 -98.05 < 2e-16 *** zcountyGI.reproductive -0.2199 0.1928 -1.14 0.25 zcountyGini 0.5295 0.0813 6.51 7.6e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1535, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.35981 0.5998 zcountyGI.reproductive 0.46988 0.6855 zcountyGini 0.03846 0.1961 Negative binomial dispersion parameter: 0.32597 (std. err.: 0.023656) Log-likelihood: -1974.12


County Model 4 with GI.reproductive

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.reproductive + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.reproductive + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.reproductive.m2rs1$residuals, center = TRUE) <= 2.96 & scale(GI.reproductive.m2rs1$residuals, center = TRUE) >= -2.96)), family = "nbinom") AIC: 3871.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.3225 0.1393 -88.49 < 2e-16 *** zcountyGI.reproductive -0.2954 0.2328 -1.27 0.20454 zcountyGini 0.2876 0.0772 3.73 0.00019 *** countyEduEmplEarnpca -0.1698 0.1001 -1.70 0.08968 . zcountyMedianagefemale -0.2694 0.0834 -3.23 0.00124 ** zcountySexratio -0.0178 0.1204 -0.15 0.88244 zcountyUrbanization 0.9005 0.1178 7.64 2.1e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1534, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 2.154e-01 0.464155 zcountyGI.reproductive 1.003e+00 1.001649 zcountyGini 2.216e-06 0.001489 Negative binomial dispersion parameter: 0.38556 (std. err.: 0.027535) Log-likelihood: -1924.856


County Model 3 with GI.college

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.college + zcountyGini + (zcountyGI.college + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.college.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.college.m1rs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGini, zcountyGI.college, countyState, countyPopulation))), family = "nbinom") AIC: 3976.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.522 0.105 -109.35 < 2e-16 *** zcountyGI.college 0.352 0.135 2.61 0.009 ** zcountyGini 0.434 0.082 5.30 1.2e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1602, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.27715 0.5265 zcountyGI.college 0.24816 0.4982 zcountyGini 0.05709 0.2389 Negative binomial dispersion parameter: 0.38266 (std. err.: 0.028438) Log-likelihood: -1981.15


County Model 4 with GI.college

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.college + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.college + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.college.m2rs2$residuals, center = TRUE) <= 2.96 & scale(GI.college.m2rs2$residuals, center = TRUE) >= -2.96, select = c(select = c(countySexyselfies, zcountyGini, zcountyGI.college, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization, countyPopulation)))), family = "nbinom") AIC: 3879.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.2578 0.1546 -79.30 < 2e-16 *** zcountyGI.college 0.0403 0.1638 0.25 0.80565 zcountyGini 0.2832 0.0791 3.58 0.00034 *** countyEduEmplEarnpca -0.1739 0.1106 -1.57 0.11581 zcountyMedianagefemale -0.1563 0.1070 -1.46 0.14424 zcountySexratio -0.0809 0.1888 -0.43 0.66817 zcountyUrbanization 0.9252 0.1372 6.74 1.6e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1546, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 3.875e-01 0.6224789 zcountyGI.college 9.971e-02 0.3157752 zcountyGini 1.515e-06 0.0012309 countyEduEmplEarnpca 6.539e-07 0.0008086 zcountyMedianagefemale 1.006e-01 0.3171593 zcountySexratio 3.539e-01 0.5948949 zcountyUrbanization 7.989e-02 0.2826553 Negative binomial dispersion parameter: 0.39164 (std. err.: 0.02954) Log-likelihood: -1924.92


County Model 3 with GI.managerial

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.managerial + zcountyGini + (zcountyGI.managerial + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.managerial.m1$residuals, center = TRUE) <= 2.96 & scale(GI.managerial.m1$residuals, center = TRUE) >= -2.96), select = c(countySexyselfies, zcountyGini, zcountyGI.managerial, countyState, countyPopulation)), family = "nbinom") AIC: 3736.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5290 0.1148 -100.44 < 2e-16 *** zcountyGI.managerial -0.1068 0.1359 -0.79 0.43 zcountyGini 0.3826 0.0863 4.43 9.3e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1537, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.33473 0.5786 zcountyGI.managerial 0.22777 0.4773 zcountyGini 0.08368 0.2893 Negative binomial dispersion parameter: 0.38154 (std. err.: 0.029481) Log-likelihood: -1861.345


County Model 4 with GI.managerial

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.managerial + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.managerial.m2rs1$residuals, center = TRUE) <= 2.96 & scale(GI.managerial.m2rs1$residuals, center = TRUE) >= -2.96, select = c(select = c(countySexyselfies, zcountyGini, zcountyGI.managerial, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization, countyPopulation)))), family = "nbinom") AIC: 3876.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.2835 0.1574 -78.03 < 2e-16 *** zcountyGI.managerial -0.0694 0.1167 -0.60 0.55171 zcountyGini 0.2803 0.0789 3.55 0.00038 *** countyEduEmplEarnpca -0.1664 0.1030 -1.61 0.10631 zcountyMedianagefemale -0.1649 0.1070 -1.54 0.12325 zcountySexratio -0.0704 0.1740 -0.40 0.68574 zcountyUrbanization 0.9237 0.1377 6.71 2e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1546, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 3.958e-01 0.629166 countyEduEmplEarnpca 3.626e-06 0.001904 zcountyMedianagefemale 1.140e-01 0.337639 zcountySexratio 3.846e-01 0.620145 zcountyUrbanization 1.051e-01 0.324130 Negative binomial dispersion parameter: 0.39115 (std. err.: 0.029315) Log-likelihood: -1925.11


County Model 3 with GI.income

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.income + zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.income.m1$residuals, center = TRUE) <= 2.96 & scale(GI.income.m1$residuals, center = TRUE) >= -2.96), select = c(countySexyselfies, zcountyGini, zcountyGI.income, countyState, countyPopulation)), family = "nbinom") AIC: 3744.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4309 0.1145 -99.82 < 2e-16 *** zcountyGI.income -0.0272 0.0886 -0.31 0.76 zcountyGini 0.4507 0.0666 6.76 1.3e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1537, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.4085 0.6391 Negative binomial dispersion parameter: 0.35668 (std. err.: 0.026388) Log-likelihood: -1867.05


County Model 4 with GI.income

Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.income + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.income.m2rs1$residuals, center = TRUE) <= 2.96 & scale(GI.income.m2rs1$residuals, center = TRUE) >= -2.96, select = c(select = c(countySexyselfies, zcountyGini, zcountyGI.income, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization, countyPopulation)))), family = "nbinom") AIC: 3876.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.2691 0.1547 -79.31 < 2e-16 *** zcountyGI.income -0.0431 0.1034 -0.42 0.67700 zcountyGini 0.2726 0.0825 3.30 0.00096 *** countyEduEmplEarnpca -0.1715 0.1038 -1.65 0.09870 . zcountyMedianagefemale -0.1630 0.1070 -1.52 0.12755 zcountySexratio -0.0742 0.1742 -0.43 0.67020 zcountyUrbanization 0.9284 0.1368 6.79 1.1e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1546, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 3.981e-01 0.6309596 countyEduEmplEarnpca 3.498e-07 0.0005914 zcountyMedianagefemale 1.140e-01 0.3376092 zcountySexratio 3.829e-01 0.6188215 zcountyUrbanization 9.982e-02 0.3159415 Negative binomial dispersion parameter: 0.39065 (std. err.: 0.029266) Log-likelihood: -1925.2


Beauty Models with Single GI Predictors

Beauty Model 3 with GI.health

Call: lm(formula = Zbeautysales ~ ZGI.health.t + Zgini.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.health.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.health.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.6867 -0.1425 -0.0386 0.0790 3.2246 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.043778 0.006379 -6.863 9.06e-12 *** ZGI.health.t 0.004824 0.006379 0.756 0.45 Zgini.t 0.057476 0.006492 8.854 < 2e-16 *** Zpopulation.t 0.190848 0.006617 28.841 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2808 on 1934 degrees of freedom Multiple R-squared: 0.3423, Adjusted R-squared: 0.3412 F-statistic: 335.5 on 3 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4 with GI.health

Call: lm(formula = Zbeautysales ~ ZGI.health.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, residuals(GI.health.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.health.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.907 -0.207 -0.057 0.104 36.211 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0129441 0.0205277 -0.631 0.528399 ZGI.health.t -0.0042620 0.0207930 -0.205 0.837616 Zgini.t 0.1083303 0.0214288 5.055 4.7e-07 *** Zpopulation.t 0.3075751 0.0230901 13.321 < 2e-16 *** EduEmplEarnpca.t 0.3226206 0.0927558 3.478 0.000516 *** Zmedianagefemale.t 0.0202951 0.0233523 0.869 0.384909 Zsexratio.t 0.0009369 0.0212019 0.044 0.964758 Zurbanization.t -0.0354832 0.0217330 -1.633 0.102698 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9033 on 1929 degrees of freedom Multiple R-squared: 0.1094, Adjusted R-squared: 0.1062 F-statistic: 33.87 on 7 and 1929 DF, p-value: < 2.2e-16


Beauty Model 3 with GI.reproductive

Call: lm(formula = Zbeautysales ~ ZGI.reproductive.t + Zgini.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.reproductive.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.reproductive.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -1.112 -0.229 -0.065 0.112 36.087 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.003195 0.021360 -0.150 0.88110 ZGI.reproductive.t -0.072683 0.023998 -3.029 0.00249 ** Zgini.t 0.111991 0.021693 5.163 2.69e-07 *** Zpopulation.t 0.337170 0.024479 13.774 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9403 on 1934 degrees of freedom Multiple R-squared: 0.1179, Adjusted R-squared: 0.1165 F-statistic: 86.16 on 3 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4 with GI.reproductive

Call: lm(formula = Zbeautysales ~ ZGI.reproductive.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, residuals(GI.reproductive.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.reproductive.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -1.040 -0.230 -0.068 0.120 36.064 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.003171 0.021306 -0.149 0.8817 ZGI.reproductive.t -0.045443 0.025903 -1.754 0.0795 . Zgini.t 0.118087 0.022190 5.322 1.15e-07 *** Zpopulation.t 0.355590 0.025754 13.807 < 2e-16 *** EduEmplEarnpca.t 0.248041 0.102559 2.419 0.0157 * Zmedianagefemale.t 0.020810 0.024247 0.858 0.3909 Zsexratio.t 0.005245 0.021955 0.239 0.8112 Zurbanization.t -0.040449 0.022477 -1.800 0.0721 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9379 on 1930 degrees of freedom Multiple R-squared: 0.1239, Adjusted R-squared: 0.1207 F-statistic: 39 on 7 and 1930 DF, p-value: < 2.2e-16


Beauty Model 3 with GI.college

Call: lm(formula = Zbeautysales ~ ZGI.college.t + Zgini.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.college.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.college.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.6168 -0.1406 -0.0382 0.0774 3.2032 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.043716 0.006355 -6.879 8.14e-12 *** ZGI.college.t 0.024725 0.006386 3.872 0.000112 *** Zgini.t 0.059319 0.006485 9.147 < 2e-16 *** Zpopulation.t 0.188884 0.006610 28.574 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2797 on 1934 degrees of freedom Multiple R-squared: 0.3471, Adjusted R-squared: 0.3461 F-statistic: 342.8 on 3 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4 with GI.college

Call: lm(formula = Zbeautysales ~ ZGI.college.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, residuals(GI.college.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.college.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.909 -0.206 -0.057 0.106 36.213 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.012911 0.020527 -0.629 0.529449 ZGI.college.t -0.002290 0.023775 -0.096 0.923260 Zgini.t 0.108599 0.021401 5.074 4.26e-07 *** Zpopulation.t 0.307713 0.023213 13.256 < 2e-16 *** EduEmplEarnpca.t 0.326431 0.096919 3.368 0.000772 *** Zmedianagefemale.t 0.019777 0.023637 0.837 0.402880 Zsexratio.t 0.002367 0.023453 0.101 0.919625 Zurbanization.t -0.035769 0.021676 -1.650 0.099063 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9033 on 1929 degrees of freedom Multiple R-squared: 0.1094, Adjusted R-squared: 0.1062 F-statistic: 33.86 on 7 and 1929 DF, p-value: < 2.2e-16


Beauty Model 3 with GI.managerial

Call: lm(formula = Zbeautysales ~ ZGI.managerial.t + Zgini.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.managerial.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.managerial.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.6591 -0.1403 -0.0401 0.0759 3.2231 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.043711 0.006361 -6.872 8.52e-12 *** ZGI.managerial.t 0.021832 0.006403 3.410 0.000664 *** Zgini.t 0.056551 0.006475 8.734 < 2e-16 *** Zpopulation.t 0.192611 0.006607 29.151 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.28 on 1934 degrees of freedom Multiple R-squared: 0.346, Adjusted R-squared: 0.345 F-statistic: 341.1 on 3 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4 with GI.managerial

Call: lm(formula = Zbeautysales ~ ZGI.managerial.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, residuals(GI.managerial.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.managerial.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.903 -0.207 -0.057 0.107 36.214 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.012905 0.020527 -0.629 0.529629 ZGI.managerial.t 0.001151 0.021372 0.054 0.957047 Zgini.t 0.108465 0.021478 5.050 4.83e-07 *** Zpopulation.t 0.307566 0.023142 13.290 < 2e-16 *** EduEmplEarnpca.t 0.322650 0.094559 3.412 0.000658 *** Zmedianagefemale.t 0.020253 0.023440 0.864 0.387666 Zsexratio.t 0.001214 0.021314 0.057 0.954599 Zurbanization.t -0.035939 0.021702 -1.656 0.097885 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9033 on 1929 degrees of freedom Multiple R-squared: 0.1094, Adjusted R-squared: 0.1062 F-statistic: 33.86 on 7 and 1929 DF, p-value: < 2.2e-16


Beauty Model 3 with GI.income

Call: lm(formula = Zbeautysales ~ ZGI.income.t + Zgini.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.income.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.income.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.6758 -0.1395 -0.0378 0.0728 3.2193 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.043901 0.006362 -6.900 7.01e-12 *** ZGI.income.t 0.021752 0.006580 3.306 0.000965 *** Zgini.t 0.056323 0.006479 8.693 < 2e-16 *** Zpopulation.t 0.196224 0.006775 28.964 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.28 on 1934 degrees of freedom Multiple R-squared: 0.3458, Adjusted R-squared: 0.3448 F-statistic: 340.7 on 3 and 1934 DF, p-value: < 2.2e-16


Beauty Model 4 with GI.income

Call: lm(formula = Zbeautysales ~ ZGI.income.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(beauty, residuals(GI.income.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.income.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.904 -0.209 -0.058 0.108 36.208 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0128454 0.0205258 -0.626 0.531510 ZGI.income.t -0.0126595 0.0224295 -0.564 0.572538 Zgini.t 0.1097169 0.0214937 5.105 3.64e-07 *** Zpopulation.t 0.3045305 0.0236674 12.867 < 2e-16 *** EduEmplEarnpca.t 0.3406426 0.0973609 3.499 0.000478 *** Zmedianagefemale.t 0.0182096 0.0235862 0.772 0.440183 Zsexratio.t 0.0005921 0.0211365 0.028 0.977655 Zurbanization.t -0.0355848 0.0216584 -1.643 0.100546 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9033 on 1929 degrees of freedom Multiple R-squared: 0.1096, Adjusted R-squared: 0.1063 F-statistic: 33.91 on 7 and 1929 DF, p-value: < 2.2e-16


Clothing Models with Single GI Predictors

Clothing Model 3 with GI.health

Call: lm(formula = Zclothingsales ~ ZGI.health.t + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.health.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.health.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.33723 -0.11871 -0.05443 0.04474 2.00520 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045689 0.006303 -7.249 7.32e-13 *** ZGI.health.t 0.006561 0.006353 1.033 0.302 Zgini.t 0.040542 0.006373 6.362 2.78e-10 *** Zpopulation.t 0.106714 0.006489 16.446 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.224 on 1260 degrees of freedom Multiple R-squared: 0.207, Adjusted R-squared: 0.2052 F-statistic: 109.7 on 3 and 1260 DF, p-value: < 2.2e-16


Clothing Model 4 with GI.health

Call: lm(formula = Zclothingsales ~ ZGI.health.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, residuals(GI.health.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.health.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.34744 -0.11483 -0.05045 0.04124 1.98742 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0450395 0.0061843 -7.283 5.75e-13 *** ZGI.health.t 0.0077500 0.0062448 1.241 0.21483 Zgini.t 0.0428920 0.0063641 6.740 2.41e-11 *** Zpopulation.t 0.1277681 0.0071857 17.781 < 2e-16 *** EduEmplEarnpca.t 0.1416013 0.0279148 5.073 4.51e-07 *** Zmedianagefemale.t 0.0204909 0.0072067 2.843 0.00454 ** Zsexratio.t -0.0005721 0.0063446 -0.090 0.92816 Zurbanization.t -0.0207475 0.0070894 -2.927 0.00349 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2198 on 1256 degrees of freedom Multiple R-squared: 0.2396, Adjusted R-squared: 0.2353 F-statistic: 56.53 on 7 and 1256 DF, p-value: < 2.2e-16


Clothing Model 3 with GI.reproductive

Call: lm(formula = Zclothingsales ~ ZGI.reproductive.t + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.reproductive.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.reproductive.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.35137 -0.11298 -0.04854 0.04216 1.96191 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045402 0.006222 -7.297 5.20e-13 *** ZGI.reproductive.t -0.041762 0.007154 -5.838 6.72e-09 *** Zgini.t 0.041767 0.006289 6.642 4.60e-11 *** Zpopulation.t 0.127704 0.007343 17.391 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2212 on 1260 degrees of freedom Multiple R-squared: 0.2273, Adjusted R-squared: 0.2254 F-statistic: 123.5 on 3 and 1260 DF, p-value: < 2.2e-16


Clothing Model 4 with GI.reproductive

Call: lm(formula = Zclothingsales ~ ZGI.reproductive.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, residuals(GI.reproductive.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.reproductive.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.35946 -0.11352 -0.04659 0.04636 1.95850 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.044935 0.006158 -7.297 5.19e-13 *** ZGI.reproductive.t -0.028049 0.007974 -3.517 0.000451 *** Zgini.t 0.042466 0.006328 6.711 2.93e-11 *** Zpopulation.t 0.139130 0.007856 17.711 < 2e-16 *** EduEmplEarnpca.t 0.095730 0.030673 3.121 0.001844 ** Zmedianagefemale.t 0.019205 0.007175 2.677 0.007535 ** Zsexratio.t -0.001183 0.006316 -0.187 0.851419 Zurbanization.t -0.021902 0.007059 -3.103 0.001959 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2188 on 1256 degrees of freedom Multiple R-squared: 0.2461, Adjusted R-squared: 0.2419 F-statistic: 58.56 on 7 and 1256 DF, p-value: < 2.2e-16


Clothing Model 3 with GI.college

Call: lm(formula = Zclothingsales ~ ZGI.college.t + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.college.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.college.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.33403 -0.11711 -0.05056 0.04196 1.99541 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045760 0.006272 -7.296 5.24e-13 *** ZGI.college.t 0.023082 0.006293 3.668 0.000255 *** Zgini.t 0.042403 0.006362 6.665 3.94e-11 *** Zpopulation.t 0.106259 0.006458 16.453 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.223 on 1260 degrees of freedom Multiple R-squared: 0.2148, Adjusted R-squared: 0.2129 F-statistic: 114.9 on 3 and 1260 DF, p-value: < 2.2e-16


Clothing Model 4 with GI.college

Call: lm(formula = Zclothingsales ~ ZGI.college.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, residuals(GI.college.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.college.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.35048 -0.11539 -0.04758 0.04119 1.97689 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045134 0.006165 -7.321 4.4e-13 *** ZGI.college.t 0.022933 0.007592 3.021 0.002574 ** Zgini.t 0.042157 0.006337 6.652 4.3e-11 *** Zpopulation.t 0.127557 0.007164 17.805 < 2e-16 *** EduEmplEarnpca.t 0.108525 0.029878 3.632 0.000292 *** Zmedianagefemale.t 0.026055 0.007442 3.501 0.000479 *** Zsexratio.t -0.011782 0.007292 -1.616 0.106428 Zurbanization.t -0.021892 0.007069 -3.097 0.001999 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2191 on 1256 degrees of freedom Multiple R-squared: 0.2441, Adjusted R-squared: 0.2399 F-statistic: 57.95 on 7 and 1256 DF, p-value: < 2.2e-16


Clothing Model 3 with GI.managerial

Call: lm(formula = Zclothingsales ~ ZGI.managerial.t + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.managerial.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.managerial.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.33615 -0.12022 -0.05375 0.04497 2.00262 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045756 0.006303 -7.259 6.78e-13 *** ZGI.managerial.t 0.006361 0.006415 0.991 0.322 Zgini.t 0.039473 0.006408 6.160 9.78e-10 *** Zpopulation.t 0.107511 0.006535 16.452 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2241 on 1260 degrees of freedom Multiple R-squared: 0.207, Adjusted R-squared: 0.2051 F-statistic: 109.6 on 3 and 1260 DF, p-value: < 2.2e-16


Clothing Model 4 with GI.managerial

Call: lm(formula = Zclothingsales ~ ZGI.managerial.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, residuals(GI.managerial.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.managerial.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.35131 -0.11469 -0.04956 0.04204 1.97546 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0451209 0.0061876 -7.292 5.38e-13 *** ZGI.managerial.t -0.0014754 0.0065761 -0.224 0.82251 Zgini.t 0.0427249 0.0064515 6.622 5.22e-11 *** Zpopulation.t 0.1275883 0.0072148 17.684 < 2e-16 *** EduEmplEarnpca.t 0.1427911 0.0286505 4.984 7.10e-07 *** Zmedianagefemale.t 0.0200347 0.0072205 2.775 0.00561 ** Zsexratio.t -0.0005446 0.0064518 -0.084 0.93275 Zurbanization.t -0.0210415 0.0070895 -2.968 0.00305 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2199 on 1256 degrees of freedom Multiple R-squared: 0.2387, Adjusted R-squared: 0.2344 F-statistic: 56.25 on 7 and 1256 DF, p-value: < 2.2e-16


Clothing Model 3 with GI.income

Call: lm(formula = Zclothingsales ~ ZGI.income.t + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.income.m1nori, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.income.m1nori, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.33189 -0.11922 -0.05490 0.04425 2.00134 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045813 0.006302 -7.270 6.29e-13 *** ZGI.income.t 0.008710 0.006967 1.250 0.211 Zgini.t 0.039510 0.006388 6.185 8.38e-10 *** Zpopulation.t 0.108921 0.006717 16.216 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.224 on 1260 degrees of freedom Multiple R-squared: 0.2074, Adjusted R-squared: 0.2055 F-statistic: 109.9 on 3 and 1260 DF, p-value: < 2.2e-16


Clothing Model 4 with GI.income

Call: lm(formula = Zclothingsales ~ ZGI.income.t + Zgini.t + Zpopulation.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t, data = na.omit(subset(clothing, residuals(GI.income.m2nori2, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.income.m2nori2, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.35075 -0.11526 -0.04897 0.04239 1.97681 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0451191 0.0061880 -7.291 5.41e-13 *** ZGI.income.t -0.0002052 0.0071806 -0.029 0.97720 Zgini.t 0.0425032 0.0064068 6.634 4.84e-11 *** Zpopulation.t 0.1276751 0.0073806 17.299 < 2e-16 *** EduEmplEarnpca.t 0.1416099 0.0292756 4.837 1.48e-06 *** Zmedianagefemale.t 0.0201142 0.0072598 2.771 0.00568 ** Zsexratio.t -0.0008158 0.0063543 -0.128 0.89787 Zurbanization.t -0.0210588 0.0070894 -2.970 0.00303 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2199 on 1256 degrees of freedom Multiple R-squared: 0.2386, Adjusted R-squared: 0.2344 F-statistic: 56.24 on 7 and 1256 DF, p-value: < 2.2e-16


Models with GI Factor

To ensure that the inclusion of five gender inequality measures at the city and county level

did not reduce the likelihood of gender inequality affecting the dependent variables, we entered

all five gender inequality variables into a principal components analysis restrained to extract one

component. We then re-ran Models 3 and 4 for city, county, beauty salon, and clothing store data

comparing this gender inequality component against income inequality (in separate models). For

Model 3, we regressed sexy selfies onto the Gini coefficient and the gender inequality

component, and for Model 4 we added all potential confounds (median income, median age,

unemployment status, educational attainment of women, the operational sex ratio, and

urbanization). Consistent with previous results, the effect of economic inequality was larger and

more reliable than the effect of the gender inequality component in every model.


City Model 3 with GI Factor

Call: glmmadmb(formula = citySexyselfies ~ cityGI.factor + zcityGini + (cityGI.factor + zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.factor.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.factor.m1rs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, cityGI.factor, cityState, cityPopulation))), family = "nbinom") AIC: 8428 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6095 0.0970 -109.42 < 2e-16 *** cityGI.factor -0.0688 0.0619 -1.11 0.27 zcityGini 0.3026 0.0548 5.52 3.4e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5396, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.30745 0.5545 cityGI.factor 0.03727 0.1931 zcityGini 0.02909 0.1706 Negative binomial dispersion parameter: 0.17578 (std. err.: 0.0088418) Log-likelihood: -4206.99


City Model 4 with GI Factor

Call: glmmadmb(formula = citySexyselfies ~ cityGI.factor + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (cityGI.factor + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(GI.factor.m2rs$residuals, center = TRUE) <= 2.96 & scale(GI.factor.m2rs$residuals, center = TRUE) >= -2.96)), family = "nbinom") AIC: 7809 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7389 0.0933 -115.12 < 2e-16 *** cityGI.factor -0.0186 0.0633 -0.29 0.7685 zcityGini 0.2807 0.0602 4.66 3.1e-06 *** cityEduEmplEarnpca -0.1991 0.0977 -2.04 0.0415 * zcityMedianagefemale -0.2433 0.0838 -2.90 0.0037 ** zcitySexratio 0.0320 0.0678 0.47 0.6368 zcityUrbanization 0.2133 0.1322 1.61 0.1065 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=4937, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.22019 0.4692 cityGI.factor 0.02669 0.1634 zcityGini 0.01556 0.1247 cityEduEmplEarnpca 0.13775 0.3711 zcityMedianagefemale 0.10283 0.3207 zcitySexratio 0.03232 0.1798 zcityUrbanization 0.25573 0.5057 Negative binomial dispersion parameter: 0.19865 (std. err.: 0.010617) Log-likelihood: -3889.49


County Model 3 with GI Factor

Call: glmmadmb(formula = countySexyselfies ~ countyGI.factor + zcountyGini + (countyGI.factor + zcountyGini | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.factor.m1rs$residuals, center = TRUE) <= 2.96 & scale(GI.factor.m1rs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGini, countyGI.factor, countyState, countyPopulation))), family = "nbinom") AIC: 4210.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.2990 0.1183 -95.49 < 2e-16 *** countyGI.factor -0.0126 0.1316 -0.10 0.92 zcountyGini 0.5236 0.0710 7.38 1.6e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1585, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.410120 0.64041 countyGI.factor 0.274760 0.52418 zcountyGini 0.001952 0.04418 Negative binomial dispersion parameter: 0.33562 (std. err.: 0.023478) Log-likelihood: -2098.306


County Model 4 with GI Factor

Call: glmmadmb(formula = countySexyselfies ~ countyGI.factor + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(GI.factor.m2$residuals, center = TRUE) <= 2.96 & scale(GI.factor.m2$residuals, center = TRUE) >= -2.96)), family = "nbinom") AIC: 3890.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.21e+01 1.48e-01 -82.06 < 2e-16 *** countyGI.factor 2.64e-02 1.04e-01 0.25 0.799 zcountyGini 3.08e-01 7.82e-02 3.94 8.1e-05 *** countyEduEmplEarnpca -1.45e-01 9.84e-02 -1.47 0.141 zcountyMedianagefemale -2.16e-01 8.49e-02 -2.55 0.011 * zcountySexratio 1.13e-04 1.22e-01 0.00 0.999 zcountyUrbanization 8.76e-01 1.19e-01 7.39 1.5e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1534, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.4344 0.6591 Negative binomial dispersion parameter: 0.35859 (std. err.: 0.024992)

Log-likelihood: -1936.264


Beauty Model 3 with GI Factor

Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod'] Formula: Zbeautysales ~ 1 + GI.factor + Zgini.t + Zpopulation.t + (GI.factor + Zgini.t - 1 | State) Data: na.omit(subset(beauty, residuals(GI.factor.m1lmrs, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.factor.m1lmrs, type = "pearson", center = TRUE) >= -2.96)) REML criterion at convergence: 5270.2 Scaled residuals: Min 1Q Median 3Q Max -1.371 -0.233 -0.060 0.130 37.892 Random effects: Groups Name Variance Std.Dev. Corr State GI.factor 0.002576 0.05076 Zgini.t 0.011707 0.10820 -1.00 Residual 0.870191 0.93284 Number of obs: 1938, groups: State, 50 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) -7.351e-03 2.176e-02 1.839e+03 -0.338 0.73549 GI.factor 1.845e-02 2.343e-02 1.226e+02 0.787 0.43257 Zgini.t 9.368e-02 2.947e-02 5.720e+01 3.179 0.00238 ** Zpopulation.t 3.118e-01 2.193e-02 1.932e+03 14.216 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) GI.fct Zgin.t GI.factor 0.008 Zgini.t 0.003 -0.264 Zpopulatn.t -0.001 0.107 -0.130


Beauty Model 4 with GI Factor

Call: lm(formula = Zbeautysales ~ GI.factor + Zgini.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t + Zpopulation.t, data = na.omit(subset(beauty, residuals(GI.factor.m2lm, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.factor.m2lm, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -1.028 -0.233 -0.065 0.120 36.084 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.003323 0.021322 -0.156 0.87617 GI.factor -0.009154 0.023553 -0.389 0.69759 Zgini.t 0.120936 0.022262 5.432 6.26e-08 *** EduEmplEarnpca.t 0.325771 0.103340 3.152 0.00164 ** Zmedianagefemale.t 0.021151 0.024549 0.862 0.38903 Zsexratio.t 0.008127 0.022319 0.364 0.71582 Zurbanization.t -0.039918 0.022553 -1.770 0.07689 . Zpopulation.t 0.337585 0.023982 14.077 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9386 on 1930 degrees of freedom Multiple R-squared: 0.1226, Adjusted R-squared: 0.1194 F-statistic: 38.52 on 7 and 1930 DF, p-value: < 2.2e-16


Clothing Model 3 with GI Factor

Call: lm(formula = Zclothingsales ~ GI.factor + Zgini.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.factor.m1lm, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.factor.m1lm, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.32534 -0.11744 -0.05256 0.04215 2.01018 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045447 0.006295 -7.220 8.99e-13 *** GI.factor 0.019418 0.006383 3.042 0.0024 ** Zgini.t 0.039845 0.006359 6.266 5.09e-10 *** Zpopulation.t 0.110026 0.006564 16.763 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2235 on 1257 degrees of freedom Multiple R-squared: 0.2125, Adjusted R-squared: 0.2107 F-statistic: 113.1 on 3 and 1257 DF, p-value: < 2.2e-16


Clothing Model 4 with GI Factor

Call: lm(formula = Zclothingsales ~ GI.factor + Zgini.t + EduEmplEarnpca.t + Zmedianagefemale.t + Zsexratio.t + Zurbanization.t + Zpopulation.t, data = na.omit(subset(clothing, residuals(GI.factor.m2lm, type = "pearson", center = TRUE) <= 2.96 & residuals(GI.factor.m2lm, type = "pearson", center = TRUE) >= -2.96))) Residuals: Min 1Q Median 3Q Max -0.34465 -0.11513 -0.05064 0.04156 1.98615 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.045116 0.006183 -7.297 5.19e-13 *** GI.factor 0.010144 0.007100 1.429 0.15335 Zgini.t 0.041372 0.006401 6.463 1.46e-10 *** EduEmplEarnpca.t 0.123194 0.030668 4.017 6.24e-05 *** Zmedianagefemale.t 0.022224 0.007346 3.025 0.00253 ** Zsexratio.t -0.003104 0.006541 -0.475 0.63522 Zurbanization.t -0.021475 0.007090 -3.029 0.00250 ** Zpopulation.t 0.129253 0.007264 17.795 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2197 on 1256 degrees of freedom Multiple R-squared: 0.2399, Adjusted R-squared: 0.2356 F-statistic: 56.62 on 7 and 1256 DF, p-value: < 2.2e-16


Models with Alternate Measures of income Inequality

To test whether the effect of income inequality on sexy selfie posting was robust to other

measures of income inequality, we re-ran models 2–4 replacing the Gini coefficient with two

alternate measure of income inequality from the ACS [32]. The first alternative measure was the

share of aggregate household income held by the top 5% of households (the Top 5% share) and

the second was the ratio of aggregate household income held by the upper quintile divided by that

held by the lowest quintile (the 80:20 ratio). Data were gathered at the city and county level and

high scores represented more economic inequality. Both the Top 5% share and the 80:20 ratio

showed a large correlation with the Gini coefficient, City: rs(5557) = .55–89, ps < .001; County:

r(1622) = .88–89, ps < .001. Consistent with our main results, both the Top 5% share and the

80:20 ratio positively and significantly predicted sexy selfie posting in every model and were

larger and more reliable predictor of sexy selfie posting frequency than all gender inequality

predictors.


Top 5 Percent Models

City Model 2 Top 5 Percent

Call: glmmadmb(formula = citySexyselfies ~ zcityTop5percent + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2.Top5.ri$residuals, center = TRUE) <= 2.96 & scale(city2.Top5.ri$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityTop5percent, cityPopulation, cityState))), family = "nbinom") AIC: 8265.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6158 0.1007 -105.4 < 2e-16 *** zcityTop5percent 0.2450 0.0395 6.2 5.5e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5509, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3627 0.6023 Negative binomial dispersion parameter: 0.19474 (std. err.: 0.009921) Log-likelihood: -4128.56



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityTop5percent + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3.Top5.rirs$residuals, center = TRUE) <= 2.96 & scale(city3.Top5.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityTop5percent, cityPopulation, cityState))), family = "nbinom") AIC: 8449.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6594 0.0924 -115.33 <2e-16 *** zcityGI.health -0.0799 0.0620 -1.29 0.197 zcityGI.reproductive 0.0528 0.0696 0.76 0.448 zcityGI.college 0.0940 0.0685 1.37 0.170 zcityGI.managerial 0.0162 0.0706 0.23 0.819 zcityGI.income -0.1265 0.0539 -2.35 0.019 * zcityTop5percent 0.2524 0.0429 5.88 4e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5396, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 2.507e-01 0.500680 zcityGI.health 2.424e-02 0.155708 zcityGI.reproductive 4.927e-02 0.221973 zcityGI.college 5.039e-02 0.224477 zcityGI.managerial 6.448e-02 0.253935 zcityGI.income 6.911e-06 0.002629 Negative binomial dispersion parameter: 0.18145 (std. err.: 0.0091679) Log-likelihood: -4210.94



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityTop5percent + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4.Top5.rirs$residuals, center = TRUE) <= 2.96 & scale(city4.Top5.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityTop5percent, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 8416.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.73105 0.08772 -122.33 < 2e-16 *** zcityGI.health -0.10392 0.06818 -1.52 0.127 zcityGI.reproductive -0.00755 0.07468 -0.10 0.919 zcityGI.college 0.17403 0.07856 2.22 0.027 * zcityGI.managerial 0.04532 0.07168 0.63 0.527 zcityGI.income -0.13339 0.06128 -2.18 0.030 * zcityTop5percent 0.22545 0.04386 5.14 2.8e-07 *** cityEduEmplEarnpca -0.19268 0.06221 -3.10 0.002 ** zcityMedianagefemale -0.22331 0.05657 -3.95 7.9e-05 *** zcitySexratio -0.14220 0.06594 -2.16 0.031 * zcityUrbanization -0.01351 0.05706 -0.24 0.813 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5372, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.20617 0.4541 zcityGI.health 0.04678 0.2163 zcityGI.reproductive 0.06708 0.2590 zcityGI.college 0.05398 0.2323 zcityGI.managerial 0.06661 0.2581 zcityGI.income 0.01452 0.1205 Negative binomial dispersion parameter: 0.19227 (std. err.: 0.009902) Log-likelihood: -4190.334


County Model 2 Top 5 Percent

Call: glmmadmb(formula = countySexyselfies ~ zcountyTop5percent + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2.Top5.ri$residuals, center = TRUE) <= 2.96 & scale(county2.Top5.ri$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyTop5percent, countyPopulation, countyState))), family = "nbinom") AIC: 4004.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4167 0.1057 -108.0 < 2e-16 *** zcountyTop5percent 0.4732 0.0696 6.8 1.1e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1602, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3335 0.5775 Negative binomial dispersion parameter: 0.34707 (std. err.: 0.024261) Log-likelihood: -1998.04



Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyTop5percent + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3.Top5.rirs$residuals, center = TRUE) <= 2.96 & scale(county3.Top5.rirs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyTop5percent, countyPopulation, countyState))), family = "nbinom") AIC: 4021 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5880 0.1193 -97.11 < 2e-16 *** zcountyGI.health -0.1420 0.1464 -0.97 0.332 zcountyGI.reproductive -0.1370 0.1804 -0.76 0.448 zcountyGI.college 0.2675 0.1378 1.94 0.052 . zcountyGI.managerial -0.0856 0.1113 -0.77 0.442 zcountyGI.income -0.0247 0.0995 -0.25 0.804 zcountyTop5percent 0.4947 0.0732 6.76 1.4e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1587, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.2897 0.5382 zcountyGI.health 0.3099 0.5567 zcountyGI.reproductive 0.4051 0.6365 zcountyGI.college 0.2380 0.4878 Negative binomial dispersion parameter: 0.39391 (std. err.: 0.029434) Log-likelihood: -1998.51



Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyTop5percent + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county4.Top5.rirs2$residuals, center = TRUE) <= 2.96 & scale(county4.Top5.rirs2$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyTop5percent, countyPopulation, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization))), family = "nbinom") AIC: 3858.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.49556 0.14690 -85.06 < 2e-16 *** zcountyGI.health -0.22181 0.15181 -1.46 0.1440 zcountyGI.reproductive -0.21825 0.23191 -0.94 0.3466 zcountyGI.college -0.01386 0.16191 -0.09 0.9318 zcountyGI.managerial -0.02711 0.12446 -0.22 0.8276 zcountyGI.income 0.00133 0.11003 0.01 0.9903 zcountyTop5percent 0.27810 0.08571 3.24 0.0012 ** countyEduEmplEarnpca -0.21746 0.10866 -2.00 0.0454 * zcountyMedianagefemale -0.22800 0.10155 -2.25 0.0248 * zcountySexratio -0.09877 0.18634 -0.53 0.5961 zcountyUrbanization 0.94449 0.13313 7.09 1.3e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1533, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.1472000 0.38367 zcountyGI.health 0.2768300 0.52615 zcountyGI.reproductive 0.9315000 0.96514 zcountyGI.college 0.0822660 0.28682 countyEduEmplEarnpca 0.0000308 0.00555 zcountyMedianagefemale 0.0743010 0.27258 zcountySexratio 0.3367600 0.58031 zcountyUrbanization 0.0409800 0.20244 Negative binomial dispersion parameter: 0.44225 (std. err.: 0.035047) Log-likelihood: -1909.14


80:20 Ratio Models

City Model 2 80:20 Ratio

Call: glmmadmb(formula = citySexyselfies ~ zcity8020ratio + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2.8020.ri$residuals, center = TRUE) <= 2.96 & scale(city2.8020.ri$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcity8020ratio, cityPopulation, cityState))), family = "nbinom") AIC: 8777.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.5689 0.0955 -110.63 < 2e-16 *** zcity8020ratio 0.4253 0.0704 6.04 1.5e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5558, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3115 0.5581 Negative binomial dispersion parameter: 0.17349 (std. err.: 0.0082692) Log-likelihood: -4384.92



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcity8020ratio + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3.8020.ri$residuals, center = TRUE) <= 2.96 & scale(city3.8020.ri$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcity8020ratio, cityPopulation, cityState))), family = "nbinom") AIC: 8512 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.5936 0.0973 -108.87 < 2e-16 *** zcityGI.health -0.0823 0.0529 -1.55 0.1199 zcityGI.reproductive 0.0938 0.0485 1.94 0.0529 . zcityGI.college 0.1553 0.0485 3.20 0.0014 ** zcityGI.managerial 0.0254 0.0518 0.49 0.6243 zcityGI.income -0.1279 0.0519 -2.47 0.0137 * zcity8020ratio 0.4259 0.0715 5.96 2.6e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5442, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3233 0.5686 Negative binomial dispersion parameter: 0.17367 (std. err.: 0.0084188) Log-likelihood: -4246.98



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcity8020ratio + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4.8020.rirs3$residuals, center = TRUE) <= 2.96 & scale(city4.8020.rirs3$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcity8020ratio, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 8375 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7292 0.0903 -118.84 < 2e-16 *** zcityGI.health -0.1121 0.0553 -2.03 0.0426 * zcityGI.reproductive 0.0562 0.0492 1.14 0.2531 zcityGI.college 0.1781 0.0656 2.71 0.0066 ** zcityGI.managerial 0.0899 0.0547 1.64 0.1004 zcityGI.income -0.1191 0.0548 -2.18 0.0296 * zcity8020ratio 0.4219 0.0782 5.39 6.9e-08 *** cityEduEmplEarnpca -0.2961 0.0915 -3.24 0.0012 ** zcityMedianagefemale -0.1650 0.0790 -2.09 0.0367 * zcitySexratio -0.0956 0.0724 -1.32 0.1865 zcityUrbanization 0.2381 0.1346 1.77 0.0768 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5369, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.21143 0.4598 cityEduEmplEarnpca 0.11780 0.3432 zcityMedianagefemale 0.08002 0.2829 zcitySexratio 0.01951 0.1397 zcityUrbanization 0.28180 0.5308 Negative binomial dispersion parameter: 0.19806 (std. err.: 0.010049) Log-likelihood: -4170.51


County Model 2 80:20 Ratio

Call: glmmadmb(formula = countySexyselfies ~ zcounty8020ratio + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2.8020.ri$residuals, center = TRUE) <= 2.96 & scale(county2.8020.ri$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcounty8020ratio, countyPopulation, countyState))), family = "nbinom") AIC: 4070 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4104 0.1094 -104.27 < 2e-16 *** zcounty8020ratio 0.4820 0.0612 7.88 3.2e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1607, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3732 0.6109 Negative binomial dispersion parameter: 0.35134 (std. err.: 0.02432) Log-likelihood: -2031



Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcounty8020ratio + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3.8020.rirs$residuals, center = TRUE) <= 2.96 & scale(county3.8020.rirs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcounty8020ratio, countyPopulation, countyState))), family = "nbinom") AIC: 4058.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.56208 0.12198 -94.79 < 2e-16 *** zcountyGI.health -0.12349 0.14919 -0.83 0.408 zcountyGI.reproductive -0.10536 0.17632 -0.60 0.550 zcountyGI.college 0.32555 0.14408 2.26 0.024 * zcountyGI.managerial -0.00734 0.11180 -0.07 0.948 zcountyGI.income -0.07728 0.09855 -0.78 0.433 zcounty8020ratio 0.43452 0.06558 6.63 3.4e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1590, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3160 0.5622 zcountyGI.health 0.3313 0.5756 zcountyGI.reproductive 0.3469 0.5890 zcountyGI.college 0.2851 0.5339 Negative binomial dispersion parameter: 0.39353 (std. err.: 0.029049) Log-likelihood: -2017.06



Call: glmmadmb(formula = countySexyselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcounty8020ratio + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, select = c(countySexyselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcounty8020ratio, countyPopulation, countyState))), family = "nbinom") AIC: 3926.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.48032 0.14649 -85.20 < 2e-16 *** zcountyGI.health -0.18896 0.15358 -1.23 0.2186 zcountyGI.reproductive -0.18848 0.22538 -0.84 0.4030 zcountyGI.college 0.02502 0.16256 0.15 0.8777 zcountyGI.managerial 0.00729 0.12277 0.06 0.9527 zcountyGI.income -0.02801 0.10859 -0.26 0.7964 zcounty8020ratio 0.20022 0.07683 2.61 0.0092 ** countyEduEmplEarnpca -0.21874 0.11041 -1.98 0.0476 * zcountyMedianagefemale -0.16116 0.10298 -1.56 0.1176 zcountySexratio -0.09599 0.18683 -0.51 0.6074 zcountyUrbanization 0.99782 0.13017 7.67 1.8e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1556, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.142810 0.37790 zcountyGI.health 0.298080 0.54597 zcountyGI.reproductive 0.818050 0.90446 zcountyGI.college 0.081636 0.28572 countyEduEmplEarnpca 0.002995 0.05473 zcountyMedianagefemale 0.071041 0.26654 zcountySexratio 0.348090 0.58999 zcountyUrbanization 0.038399 0.19596 Negative binomial dispersion parameter: 0.43704 (std. err.: 0.034374) Log-likelihood: -1943.18

NB: No outliers were removed from this model (their removal affected the model convergence.)


Models with Additional Pornography Exclusions

In the main manuscript, we excluded all posts containing the words “porn”, “xxx”, and

“adult”. Here, we run two additional robustness tests: First, we exclude an additional range or

pornography keywords from post text; second, we model Instragram data only.

Models Excluding Pornography Keywords

As an additional robustness test, we then excluded all posts containing the following

words: “pussy”, “sexhookup”, “cum”, “skypeme”, “skypesex”, “phonesex”, “camgirl”,

“cammodel”, and “adultsonly”. We then re-ran sexy analyses at the city, county, and nation level,

comparing the effect sizes of income inequality versus gender inequality between the initial

models and those using these restricted sexy selfie counts. At the city and county level, findings

replicate those in the main submission. At the cross-national level, the main effect of income

inequality does not reach conventional statistical significance (see Model 2) but the income

inequality × development interaction is robust (see Model 4).


City Model 1 with Additional Pornography Exclusions

Call: glmmadmb(formula = citySexyselfies.restricted ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city1r.rirs$residuals, center = TRUE) <= 2.96 & scale(city1r.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies.restricted, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, cityPopulation, cityState))), family = "nbinom") AIC: 8341.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6686 0.0878 -121.50 <2e-16 *** zcityGI.health -0.0624 0.0590 -1.06 0.290 zcityGI.reproductive 0.0460 0.0667 0.69 0.490 zcityGI.college 0.0985 0.0597 1.65 0.099 . zcityGI.managerial 0.0507 0.0655 0.77 0.438 zcityGI.income -0.1679 0.0584 -2.88 0.004 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5401, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.22453 0.4738 zcityGI.health 0.01700 0.1304 zcityGI.reproductive 0.04184 0.2046 zcityGI.college 0.02208 0.1486 zcityGI.managerial 0.04453 0.2110 zcityGI.income 0.01398 0.1182 Negative binomial dispersion parameter: 0.18583 (std. err.: 0.0095029) Log-likelihood: -4157.8



Call: glmmadmb(formula = citySexyselfies.restricted ~ zcityGini + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2r.ri$residuals, center = TRUE) <= 2.96 & scale(city2r.ri$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies.restricted, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 8051 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7157 0.0953 -112.45 <2e-16 *** zcityGini 0.3392 0.0402 8.44 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5515, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3163 0.5624 Negative binomial dispersion parameter: 0.217 (std. err.: 0.011451) Log-likelihood: -4021.49



Call: glmmadmb(formula = citySexyselfies.restricted ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3r.rirs$residuals, center = TRUE) <= 2.96 & scale(city3r.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies.restricted, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityPopulation, ityState))), family = "nbinom") AIC: 8278.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.7454 0.0889 -120.87 < 2e-16 *** zcityGI.health -0.0734 0.0604 -1.21 0.225 zcityGI.reproductive 0.0397 0.0685 0.58 0.562 zcityGI.college 0.1065 0.0613 1.74 0.082 . zcityGI.managerial 0.0340 0.0654 0.52 0.603 zcityGI.income -0.1276 0.0572 -2.23 0.026 * zcityGini 0.3391 0.0433 7.83 4.8e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5400, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.22613 0.4755 zcityGI.health 0.02397 0.1548 zcityGI.reproductive 0.04947 0.2224 zcityGI.college 0.02646 0.1627 zcityGI.managerial 0.04558 0.2135 zcityGI.income 0.01175 0.1084 Negative binomial dispersion parameter: 0.19826 (std. err.: 0.010338) Log-likelihood: -4125.39



Call: glmmadmb(formula = citySexyselfies.restricted ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4r.rirs3$residuals, center = TRUE) <= 2.96 & scale(city4r.rirs3$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies.restricted, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 8220.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.8536 0.0853 -127.21 < 2e-16 *** zcityGI.health -0.0937 0.0562 -1.67 0.0952 . zcityGI.reproductive -0.0106 0.0714 -0.15 0.8818 zcityGI.college 0.1353 0.0677 2.00 0.0457 * zcityGI.managerial 0.0917 0.0607 1.51 0.1306 zcityGI.income -0.1298 0.0602 -2.16 0.0311 * zcityGini 0.3200 0.0479 6.68 2.5e-11 *** cityEduEmplEarnpca -0.2130 0.0881 -2.42 0.0157 * zcityMedianagefemale -0.2266 0.0759 -2.99 0.0028 ** zcitySexratio -0.0498 0.0702 -0.71 0.4778 zcityUrbanization 0.1799 0.1265 1.42 0.1551 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5378, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.165390 0.40668 zcityGI.health 0.002336 0.04834 zcityGI.reproductive 0.053419 0.23113 zcityGI.college 0.008316 0.09119 zcityGI.managerial 0.018747 0.13692 zcityGI.income 0.014423 0.12010 cityEduEmplEarnpca 0.088353 0.29724 zcityMedianagefemale 0.067444 0.25970 zcitySexratio 0.012382 0.11127 zcityUrbanization 0.240010 0.48991 Negative binomial dispersion parameter: 0.21892 (std. err.: 0.011752) Log-likelihood: -4088.408


County Model 1 with Additional Pornography Exclusions

Call: glmmadmb(formula = countySexyselfies.restricted ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county1r.rirs$residuals, center = TRUE) <= 2.96 & scale(county1r.rirs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies.restricted, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, countyPopulation, countyState))), family = "nbinom") AIC: 4176.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5154 0.1159 -99.34 <2e-16 *** zcountyGI.health -0.0798 0.1564 -0.51 0.610 zcountyGI.reproductive -0.0826 0.1812 -0.46 0.649 zcountyGI.college 0.3175 0.1379 2.30 0.021 * zcountyGI.managerial -0.0842 0.1306 -0.64 0.519 zcountyGI.income -0.3012 0.1176 -2.56 0.010 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1588, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.21629 0.4651 zcountyGI.health 0.37080 0.6089 zcountyGI.reproductive 0.32981 0.5743 zcountyGI.college 0.20672 0.4547 zcountyGI.managerial 0.08959 0.2993 zcountyGI.income 0.14111 0.3756 Negative binomial dispersion parameter: 0.35655 (std. err.: 0.026806) Log-likelihood: -2075.29



Call: glmmadmb(formula = countySexyselfies.restricted ~ zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2r.ri$residuals, center = TRUE) <= 2.96 & scale(county2r.ri$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies.restricted, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 3909.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.4673 0.1039 -110.33 < 2e-16 *** zcountyGini 0.4752 0.0615 7.72 1.1e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1602, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.3297 0.5742 Negative binomial dispersion parameter: 0.37842 (std. err.: 0.027216) Log-likelihood: -1950.74



Call: glmmadmb(formula = countySexyselfies.restricted ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial +

zcountyGI.income + zcountyGini + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3r.rirs$residuals, center = TRUE) <= 2.96 & scale(county3r.rirs$residuals,

center = TRUE) >= -2.96, select = c(countySexyselfies.restricted, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 4109.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5916 0.1164 -99.58 <2e-16 *** zcountyGI.health -0.1155 0.1455 -0.79 0.4273 zcountyGI.reproductive -0.0326 0.1808 -0.18 0.8569 zcountyGI.college 0.3960 0.1460 2.71 0.0067 ** zcountyGI.managerial -0.0885 0.1267 -0.70 0.4847 zcountyGI.income -0.0328 0.1141 -0.29 0.7741 zcountyGini 0.5682 0.0691 8.22 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1588, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.23030 0.4799 zcountyGI.health 0.28409 0.5330 zcountyGI.reproductive 0.34838 0.5902 zcountyGI.college 0.28383 0.5328 zcountyGI.managerial 0.07535 0.2745 zcountyGI.income 0.08732 0.2955 Negative binomial dispersion parameter: 0.39569 (std. err.: 0.030272) Log-likelihood: -2040.89



Call: glmmadmb(formula = countySexyselfies.restricted ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county4r.rirs$residuals, center = TRUE) <= 2.96 & scale(county4r.rirs$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies.restricted, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization))), family = "nbinom") AIC: 3775.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.47234 0.14550 -85.72 < 2e-16 *** zcountyGI.health -0.17488 0.15564 -1.12 0.26118 zcountyGI.reproductive -0.17434 0.22769 -0.77 0.44386 zcountyGI.college 0.05679 0.16767 0.34 0.73485 zcountyGI.managerial 0.03912 0.12070 0.32 0.74585 zcountyGI.income 0.00453 0.11952 0.04 0.96979 zcountyGini 0.29280 0.08046 3.64 0.00027 *** countyEduEmplEarnpca -0.23225 0.10552 -2.20 0.02774 * zcountyMedianagefemale -0.24111 0.08577 -2.81 0.00494 ** zcountySexratio -0.04608 0.15100 -0.31 0.76026 zcountyUrbanization 0.93487 0.12410 7.53 4.9e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1536, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.12948 0.3598 zcountyGI.health 0.37301 0.6107 zcountyGI.reproductive 0.86918 0.9323 zcountyGI.college 0.21674 0.4656 zcountyGI.managerial 0.01081 0.1040 zcountyGI.income 0.08967 0.2994 Negative binomial dispersion parameter: 0.45122 (std. err.: 0.035784) Log-likelihood: -1869.77


Nation Model 1 with Additional Pornography Exclusions

Call: glmmadmb(formula = sexySelfies.restricted ~ devfactor + GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation1r.fe$residuals, center = TRUE) <= 2.96 & scale(nation1r.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.restricted, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1408.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.418 0.129 41.92 < 2e-16 *** devfactor 1.176 0.239 4.93 8.4e-07 *** GIFac.womenstats 0.418 0.225 1.85 0.064 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=108 Negative binomial dispersion parameter: 0.55633 (std. err.: 0.063979) Log-likelihood: -700.466



Call: glmmadmb(formula = sexySelfies.restricted ~ devfactor + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation2r.fe$residuals, center = TRUE) <= 2.96 & scale(nation2r.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.restricted, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1418.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.368 0.128 41.80 < 2e-16 *** devfactor 0.862 0.153 5.62 1.9e-08 *** ZGINI 0.259 0.143 1.81 0.07 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=110 Negative binomial dispersion parameter: 0.55251 (std. err.: 0.062942) Log-likelihood: -705.388



Call: glmmadmb(formula = sexySelfies.restricted ~ devfactor + GIFac.womenstats + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation3r.fe$residuals, center = TRUE) <= 2.96 & scale(nation3r.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.restricted, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1396.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.404 0.128 42.10 < 2e-16 *** devfactor 1.225 0.239 5.13 2.9e-07 *** GIFac.womenstats 0.383 0.223 1.72 0.086 . ZGINI 0.249 0.144 1.73 0.084 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=107 Negative binomial dispersion parameter: 0.57049 (std. err.: 0.066114) Log-likelihood: -693.272



Call: glmmadmb(formula = sexySelfies.restricted ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation4r.ri$residuals, center = TRUE) <= 2.96 & scale(nation4r.ri$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.restricted, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1398.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.147 0.266 19.35 < 2e-16 *** devfactor 1.442 0.256 5.63 1.8e-08 *** GIFac.womenstats 0.144 0.241 0.60 0.54962 ZGINI 0.803 0.218 3.69 0.00022 *** devfactor:ZGINI 0.720 0.236 3.05 0.00230 ** devfactor:GIFac.womenstats -0.454 0.224 -2.02 0.04315 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=108, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.3003 0.548 Negative binomial dispersion parameter: 0.67925 (std. err.: 0.085855) Log-likelihood: -691.143


Instagram-Only Models

As a secondary test to exclude potential pornographic content, we re-ran our analyses

using data only from Instagram. Unlike Twitter, Instagram is owned by Facebook. Facebook and

Instagram have a strict no nudity/pornography policy (see https://help.instagram.com/

477434105621119), meaning that the possibility of these posts being pornographic in nature is

extremely unlikely. Only cross-national data were available. Consistent with all other analyses,

there was a significant interaction between income inequality and development on Instagram sexy

selfies, see Model 4. We also found a positive association between gender inequality and sexy

selfies in Models 1 and 3, however this effect was no longer significant once including the

income inequality × development interaction in Model 4.


Nation Model 1 with Instagram-Only

Call: glmmadmb(formula = sexySelfies.insta ~ devfactor + GIFac.womenstats + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation1insta.ri$residuals, center = TRUE) <= 2.96 & scale(nation1insta.ri$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.insta, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1217.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.325 0.255 16.96 < 2e-16 *** devfactor 1.783 0.307 5.81 6.4e-09 *** GIFac.womenstats 0.699 0.271 2.58 0.01 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=107, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.6819 0.8258 Negative binomial dispersion parameter: 0.56157 (std. err.: 0.074103) Log-likelihood: -603.533


Call: glmmadmb(formula = sexySelfies.insta ~ devfactor + ZGINI + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation2insta.ri$residuals, center = TRUE) <= 2.96 & scale(nation2insta.ri$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.insta, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1188.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.3133 0.2587 16.67 < 2e-16 *** devfactor 1.2599 0.2408 5.23 1.7e-07 *** ZGINI 0.0794 0.2128 0.37 0.71 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=106, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.6924 0.8321 Negative binomial dispersion parameter: 0.52607 (std. err.: 0.070711) Log-likelihood: -589.318



Call: glmmadmb(formula = sexySelfies.insta ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation3insta.ri$residuals, center = TRUE) <= 2.96 & scale(nation3insta.ri$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.insta, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1218.6 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.309 0.260 16.56 < 2e-16 *** devfactor 1.796 0.308 5.83 5.6e-09 *** GIFac.womenstats 0.672 0.273 2.46 0.014 * ZGINI 0.148 0.212 0.70 0.485 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=107, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.7226 0.8501 Negative binomial dispersion parameter: 0.56695 (std. err.: 0.075177) Log-likelihood: -603.284


Call: glmmadmb(formula = sexySelfies.insta ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation4insta.ri$residuals, center = TRUE) <= 2.96 & scale(nation4insta.ri$residuals, center = TRUE) >= -2.96, select = c(sexySelfies.insta, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1225 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.498 0.319 14.11 < 2e-16 *** devfactor 1.757 0.296 5.94 2.9e-09 *** GIFac.womenstats 0.311 0.274 1.14 0.255 ZGINI 0.491 0.246 1.99 0.046 * devfactor:ZGINI 0.794 0.268 2.97 0.003 ** devfactor:GIFac.womenstats -0.182 0.253 -0.72 0.471 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=108, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.5224 0.7228 Negative binomial dispersion parameter: 0.58752 (std. err.: 0.078849) Log-likelihood: -604.488


Models with Unique Users Only

We aggregated tweets to the city, county, or cross-national level, and some tweets were

posted by the same users, thus violating the assumption of independence. To check that this

violation did not bias our findings, we reanalyzed our data restricting the sexy selfie counts to

users who posted only once (“unique users”). Across city, county, and nation datasets, between

76–80% of all users posted only one post. The correlation between sexy selfie counts by all users

and sexy selfie counts by users who posted only once ranged from rs = .84–.99. As shown on the

following pages, findings were highly consistent with those reported in the original submission.

There was a significant main effect of income inequality at the city and county level which was

larger in effect size than the effects of gender inequality and robust to the inclusion of confounds.

There was also a significant income inequality × development interaction at the cross-national

level, and no effect for gender inequality. Thus, excluding users who posted multiple sexy selfies

does not appear to unduly influence our findings: When we remove this source of variation, our

effects remain.


City Model 1 with Unique Users Only

Call: glmmadmb(formula = uniqueSelfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city1unq.ri$residuals, center = TRUE) <= 2.96 & scale(city1unq.ri$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, cityPopulation, cityState))), family = "nbinom") AIC: 5857 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.41150 0.06442 -177.15 < 2e-16 *** zcityGI.health -0.01266 0.04678 -0.27 0.79 zcityGI.reproductive 0.01921 0.04196 0.46 0.65 zcityGI.college -0.00808 0.04130 -0.20 0.84 zcityGI.managerial 0.11932 0.04635 2.57 0.01 * zcityGI.income -0.19449 0.04506 -4.32 1.6e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5423, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.1121 0.3348 Negative binomial dispersion parameter: 0.79236 (std. err.: 0.072199) Log-likelihood: -2920.52



Call: glmmadmb(formula = uniqueSelfies ~ zcityGini + (zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2unq.rirs$residuals, center = TRUE) <= 2.96 & scale(city2unq.rirs$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 5734.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.5271 0.0648 -178.0 < 2e-16 *** zcityGini 0.3168 0.0411 7.7 1.3e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5536, cityState=50 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.11099 0.3332 zcityGini 0.01588 0.1260 Negative binomial dispersion parameter: 1.355 (std. err.: 0.17053) Log-likelihood: -2862.07



Call: glmmadmb(formula = uniqueSelfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + (zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3unq.rirs$residuals, center = TRUE) <= 2.96 & scale(city3unq.rirs$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 5735.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.53482 0.06628 -174.02 < 2e-16 *** zcityGI.health 0.00387 0.04744 0.08 0.93498 zcityGI.reproductive 0.01790 0.04167 0.43 0.66752 zcityGI.college -0.02297 0.04168 -0.55 0.58160 zcityGI.managerial 0.10106 0.04621 2.19 0.02875 * zcityGI.income -0.16007 0.04414 -3.63 0.00029 *** zcityGini 0.33418 0.04448 7.51 5.8e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5422, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.11392 0.3375 zcityGini 0.02249 0.1500 Negative binomial dispersion parameter: 1.1448 (std. err.: 0.12714) Log-likelihood: -2857.63



Call: glmmadmb(formula = uniqueSelfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGini | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4unq.rirs2$residuals, center = TRUE) <= 2.96 & scale(city4unq.rirs2$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 5716.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.57248 0.06734 -171.85 < 2e-16 *** zcityGI.health -0.00778 0.04870 -0.16 0.87304 zcityGI.reproductive 0.01349 0.04328 0.31 0.75522 zcityGI.college -0.02247 0.05153 -0.44 0.66287 zcityGI.managerial 0.11663 0.04663 2.50 0.01238 * zcityGI.income -0.15223 0.04564 -3.34 0.00085 *** zcityGini 0.29455 0.04924 5.98 2.2e-09 *** cityEduEmplEarnpca 0.00977 0.04707 0.21 0.83555 zcityMedianagefemale -0.18745 0.04529 -4.14 3.5e-05 *** zcitySexratio -0.05693 0.05601 -1.02 0.30941 zcityUrbanization -0.05013 0.04673 -1.07 0.28341 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5402, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.11361 0.3371 zcityGini 0.03266 0.1807 Negative binomial dispersion parameter: 1.2105 (std. err.: 0.138) Log-likelihood: -2844.129


County Model 1 with Unique Users Only

Call: glmmadmb(formula = uniqueSelfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county1unq.ri$residuals, center = TRUE) <= 2.96 & scale(county1unq.ri$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, countyPopulation, countyState))), family = "nbinom") AIC: 2902.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.1996 0.0776 -157.17 < 2e-16 *** zcountyGI.health 0.0524 0.0842 0.62 0.5340 zcountyGI.reproductive 0.0217 0.1125 0.19 0.8469 zcountyGI.college 0.2342 0.0806 2.91 0.0037 ** zcountyGI.managerial -0.0601 0.0944 -0.64 0.5242 zcountyGI.income -0.3482 0.0753 -4.62 3.8e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1590, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.08789 0.2965 Negative binomial dispersion parameter: 1.2319 (std. err.: 0.14516) Log-likelihood: -1443.47



Call: glmmadmb(formula = uniqueSelfies ~ zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2unq.ri$residuals, center = TRUE) <= 2.96 & scale(county2unq.ri$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 2772.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.1964 0.0640 -190.50 <2e-16 *** zcountyGini 0.3879 0.0465 8.35 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1608, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.0803 0.2834 Negative binomial dispersion parameter: 1.6799 (std. err.: 0.23151) Log-likelihood: -1382.21



Call: glmmadmb(formula = uniqueSelfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3unq.ri$residuals, center = TRUE) <= 2.96 & scale(county3unq.ri$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 2842.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.2523 0.0757 -161.83 < 2e-16 *** zcountyGI.health 0.0390 0.0833 0.47 0.63968 zcountyGI.reproductive 0.0521 0.1121 0.46 0.64230 zcountyGI.college 0.2708 0.0808 3.35 0.00081 *** zcountyGI.managerial -0.0215 0.0934 -0.23 0.81780 zcountyGI.income -0.1428 0.0773 -1.85 0.06483 . zcountyGini 0.3890 0.0498 7.81 5.7e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1590, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.07828 0.2798 Negative binomial dispersion parameter: 1.4588 (std. err.: 0.18224) Log-likelihood: -1412.36



Call: glmmadmb(formula = uniqueSelfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county4unq.ri$residuals, center = TRUE) <= 2.96 & scale(county4unq.ri$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization))), family = "nbinom") AIC: 2625 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.7379 0.1110 -114.76 < 2e-16 *** zcountyGI.health 0.0143 0.0905 0.16 0.87430 zcountyGI.reproductive 0.0184 0.1288 0.14 0.88618 zcountyGI.college 0.1520 0.1147 1.33 0.18513 zcountyGI.managerial 0.0297 0.1028 0.29 0.77300 zcountyGI.income -0.1147 0.0820 -1.40 0.16173 zcountyGini 0.2079 0.0575 3.61 0.00030 *** countyEduEmplEarnpca -0.0928 0.0714 -1.30 0.19344 zcountyMedianagefemale -0.2809 0.0629 -4.47 7.9e-06 *** zcountySexratio -0.1179 0.1262 -0.93 0.35014 zcountyUrbanization 0.3511 0.0957 3.67 0.00024 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1542, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.0614 0.2478 Negative binomial dispersion parameter: 1.5907 (std. err.: 0.21069) Log-likelihood: -1299.499


Nation Model 1 with Unique Users Only

Call: glmmadmb(formula = uniqueSelfies ~ devfactor + GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation1unq.fe$residuals, center = TRUE) <= 2.96 & scale(nation1unq.fe$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1190 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.389 0.122 36.01 < 2e-16 *** devfactor 0.990 0.216 4.59 4.3e-06 *** GIFac.womenstats 0.253 0.207 1.23 0.22 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=107 Negative binomial dispersion parameter: 0.63411 (std. err.: 0.076162) Log-likelihood: -590.979



Call: glmmadmb(formula = uniqueSelfies ~ devfactor + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation2unq.fe$residuals, center = TRUE) <= 2.96 & scale(nation2unq.fe$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1202.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.369 0.122 35.81 < 2e-16 *** devfactor 0.832 0.144 5.78 7.7e-09 *** ZGINI 0.155 0.137 1.13 0.26 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=109 Negative binomial dispersion parameter: 0.62038 (std. err.: 0.073721) Log-likelihood: -597.151



Call: glmmadmb(formula = uniqueSelfies ~ devfactor + GIFac.womenstats + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation3unq.fe$residuals, center = TRUE) <= 2.96 & scale(nation3unq.fe$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1190.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.381 0.121 36.13 < 2e-16 *** devfactor 1.004 0.216 4.64 3.4e-06 *** GIFac.womenstats 0.201 0.208 0.97 0.33 ZGINI 0.167 0.139 1.20 0.23 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=107 Negative binomial dispersion parameter: 0.64094 (std. err.: 0.0771) Log-likelihood: -590.233



Call: glmmadmb(formula = uniqueSelfies ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation4unq.fe$residuals, center = TRUE) <= 2.96 & scale(nation4unq.fe$residuals, center = TRUE) >= -2.96, select = c(uniqueSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1164.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.1664 0.2402 17.35 <2e-16 *** devfactor 1.2337 0.2434 5.07 4e-07 *** GIFac.womenstats 0.0854 0.2317 0.37 0.7126 ZGINI 0.6338 0.2174 2.92 0.0035 ** devfactor:ZGINI 0.5414 0.2266 2.39 0.0169 * devfactor:GIFac.womenstats -0.3799 0.2137 -1.78 0.0755 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=105, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.2665 0.5163 Negative binomial dispersion parameter: 0.74733 (std. err.: 0.098984) Log-likelihood: -574.204


Models with Posts from Women of Women

Though the majority of users had female names, we did not exclude posts by users with

male names (or by users where gender was indeterminable). To see if our results replicated in a

sample that was predominantly women posting photos of themselves, we re-ran our analyses,

restricting the sample to female users posting selfies with female keywords in the post text. This

procedure approximated women posting photos of themselves, but only utilized 10% of the data

(as gender identifiers and female keywords were not available for every post).

At the city and cross-national level, we replicate our main findings in every model (i.e.,

main effects for income inequality at the city level, and income inequality × development

interaction cross-nationally). Indeed, in this restricted sample, the relationship between income

inequality and sexy selfies is larger than it is when the entire sample is analyzed. At the county

level, we replicate our main findings in Models 1–3: Once again, income inequality is associated

with sexy selfies and has a relatively larger effect than gender inequality. However, the

significance of the income inequality effect is lost in Model 4 (i.e., once confounds are included).

Whether this lost effect is meaningful or is a statistical anomaly is unclear; though the

replicability of our results across the other 129 analyses in this Model Appendix lends support to

the latter interpretation.


City Model 1 from Women of Women

Call: glmmadmb(formula = cityFfselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city1.ri$residuals, center = TRUE) <= 2.96 & scale(city1.ri$residuals, center = TRUE) >= -2.96, select = c(cityFfselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, cityPopulation, cityState))), family = "nbinom") AIC: 3852.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.9541 0.1258 -95.01 <2e-16 *** zcityGI.health -0.1359 0.0965 -1.41 0.1590 zcityGI.reproductive 0.0981 0.0859 1.14 0.2535 zcityGI.college -0.0243 0.0928 -0.26 0.7930 zcityGI.managerial 0.1856 0.0924 2.01 0.0447 * zcityGI.income -0.2820 0.0917 -3.08 0.0021 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5409, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3816 0.6178 Negative binomial dispersion parameter: 0.07075 (std. err.: 0.0054668) Log-likelihood: -1918.45



Call: glmmadmb(formula = cityFfselfies ~ zcityGini + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city2.ri$residuals, center = TRUE) <= 2.96 & scale(city2.ri$residuals, center = TRUE) >= -2.96, select = c(cityFfselfies, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 3558 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.1639 0.1317 -92.36 < 2e-16 *** zcityGini 0.4852 0.0716 6.77 1.3e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5526, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.4774 0.6909 Negative binomial dispersion parameter: 0.098157 (std. err.: 0.0082862) Log-likelihood: -1775.02



Call: glmmadmb(formula = cityFfselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + (1 | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city3.ri$residuals, center = TRUE) <= 2.96 & scale(city3.ri$residuals, center = TRUE) >= -2.96, select = c(cityFfselfies, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityPopulation, cityState))), family = "nbinom") AIC: 3824 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.05798 0.12783 -94.33 < 2e-16 *** zcityGI.health -0.12589 0.09668 -1.30 0.193 zcityGI.reproductive 0.08950 0.08498 1.05 0.292 zcityGI.college 0.00115 0.09320 0.01 0.990 zcityGI.managerial 0.14877 0.09222 1.61 0.107 zcityGI.income -0.21167 0.09156 -2.31 0.021 * zcityGini 0.42099 0.07852 5.36 8.3e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=5409, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.3755 0.6128 Negative binomial dispersion parameter: 0.074855 (std. err.: 0.005819) Log-likelihood: -1903.02



Call: glmmadmb(formula = cityFfselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(city4.rirs$residuals, center = TRUE) <= 2.96 & scale(city4.rirs$residuals, center = TRUE) >= -2.96, select = c(cityFfselfies, cityPopulation, zcityGI.health, zcityGI.reproductive, zcityGI.college, zcityGI.managerial, zcityGI.income, zcityGini, cityEduEmplEarnpca, zcityMedianagefemale, zcitySexratio, zcityUrbanization, cityState))), family = "nbinom") AIC: 3822.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.31025 0.13845 -88.91 < 2e-16 *** zcityGI.health -0.14041 0.09999 -1.40 0.1602 zcityGI.reproductive 0.05392 0.08962 0.60 0.5474 zcityGI.college -0.00864 0.11625 -0.07 0.9407 zcityGI.managerial 0.27748 0.09752 2.85 0.0044 ** zcityGI.income -0.25555 0.09820 -2.60 0.0093 ** zcityGini 0.43303 0.08632 5.02 5.3e-07 *** cityEduEmplEarnpca -0.03401 0.13333 -0.26 0.7987 zcityMedianagefemale -0.37260 0.12067 -3.09 0.0020 ** zcitySexratio -0.05988 0.12220 -0.49 0.6241 zcityUrbanization 0.56857 0.27375 2.08 0.0378 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5388, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 3.164e-01 0.562494 cityEduEmplEarnpca 3.418e-02 0.184878 zcityMedianagefemale 7.946e-02 0.281894 zcitySexratio 2.579e-05 0.005079 zcityUrbanization 9.422e-01 0.970660 Negative binomial dispersion parameter: 0.087473 (std. err.: 0.0069796) Log-likelihood: -1894.449


County Model 1 from Women of Women

Call: glmmadmb(formula = countyFfselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county1.ri$residuals, center = TRUE) <= 2.96 & scale(county1.ri$residuals, center = TRUE) >= -2.96, select = c(countyFfselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, countyPopulation, countyState))), family = "nbinom") AIC: 2269.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.764 0.172 -74.08 < 2e-16 *** zcountyGI.health -0.191 0.178 -1.07 0.28293 zcountyGI.reproductive 0.150 0.227 0.66 0.50919 zcountyGI.college 0.338 0.172 1.96 0.04965 * zcountyGI.managerial -0.275 0.178 -1.55 0.12194 zcountyGI.income -0.618 0.159 -3.88 0.00011 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1588, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.5683 0.7539 Negative binomial dispersion parameter: 0.15199 (std. err.: 0.014322) Log-likelihood: -1126.55



Call: glmmadmb(formula = countyFfselfies ~ zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county2.ri$residuals, center = TRUE) <= 2.96 & scale(county2.ri$residuals, center = TRUE) >= -2.96, select = c(countyFfselfies, zcountyGini, countyPopulation, countyState))),family = "nbinom") AIC: 1886.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.939 0.118 -109.78 < 2e-16 *** zcountyGini 0.497 0.088 5.65 1.6e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1602, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.2162 0.465 Negative binomial dispersion parameter: 0.2423 (std. err.: 0.027077) Log-likelihood: -939.047



Call: glmmadmb(formula = countyFfselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county3.ri$residuals, center = TRUE) <= 2.96 & scale(county3.ri$residuals, center = TRUE) >= -2.96, select = c(countyFfselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState))), family = "nbinom") AIC: 2223.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.857 0.176 -73.05 < 2e-16 *** zcountyGI.health -0.171 0.178 -0.96 0.337 zcountyGI.reproductive 0.117 0.224 0.52 0.601 zcountyGI.college 0.438 0.174 2.52 0.012 * zcountyGI.managerial -0.253 0.178 -1.42 0.155 zcountyGI.income -0.371 0.165 -2.25 0.025 * zcountyGini 0.528 0.116 4.55 5.3e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1581, countyState=51 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.6113 0.7819 Negative binomial dispersion parameter: 0.16411 (std. err.: 0.015689) Log-likelihood: -1102.55



Call: glmmadmb(formula = countyFfselfies ~ zcountyGI.health + zcountyGI.reproductive + zcountyGI.college + zcountyGI.managerial + zcountyGI.income + zcountyGini + countyEduEmplEarnpca + zcountyMedianagefemale + zcountySexratio + zcountyUrbanization + (1 | countyState) + offset(log(countyPopulation)), data = na.omit(subset(county, scale(county4.ri$residuals, center = TRUE) <= 2.96 & scale(county4.ri$residuals, center = TRUE) >= -2.96, select = c(countyFfselfies, zcountyGI.health, zcountyGI.reproductive, zcountyGI.college, zcountyGI.managerial, zcountyGI.income, zcountyGini, countyPopulation, countyState, countyEduEmplEarnpca, zcountyMedianagefemale, zcountySexratio, zcountyUrbanization))), family = "nbinom") AIC: 1989 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.3393 0.2668 -53.75 < 2e-16 *** zcountyGI.health -0.3287 0.1981 -1.66 0.097 . zcountyGI.reproductive -0.1381 0.2731 -0.51 0.613 zcountyGI.college -0.1227 0.2522 -0.49 0.627 zcountyGI.managerial 0.0176 0.2033 0.09 0.931 zcountyGI.income -0.4074 0.1860 -2.19 0.028 * zcountyGini 0.0954 0.1386 0.69 0.491 countyEduEmplEarnpca -0.2716 0.1763 -1.54 0.123 zcountyMedianagefemale -0.3348 0.1462 -2.29 0.022 * zcountySexratio -0.2013 0.2804 -0.72 0.473 zcountyUrbanization 1.4649 0.2207 6.64 3.2e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1529, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.5292 0.7274 Negative binomial dispersion parameter: 0.18759 (std. err.: 0.018869) Log-likelihood: -981.503


Nation Model 1 from Women of Women

Call: glmmadmb(formula = ffSelfies ~ devfactor + GIFac.womenstats + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, select = c(ffSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 977.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.239 0.223 14.50 < 2e-16 *** devfactor 1.713 0.297 5.76 8.3e-09 *** GIFac.womenstats 0.256 0.273 0.94 0.35 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=109, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.3715 0.6095 Negative binomial dispersion parameter: 0.48352 (std. err.: 0.066756) Log-likelihood: -483.613 NB: Residual outliers were not excluded for this model as they accounted for>20% of the dataset.



Call: glmmadmb(formula = ffSelfies ~ devfactor + ZGINI + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation2.ri$residuals, center = TRUE) <= 2.96 & scale(nation2.ri$residuals, center = TRUE) >= -2.96, select = c(ffSelfies, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 972.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.141 0.241 13.04 < 2e-16 *** devfactor 1.639 0.241 6.81 9.6e-12 *** ZGINI 0.391 0.242 1.62 0.11 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=109, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.4929 0.7021 Negative binomial dispersion parameter: 0.5007 (std. err.: 0.070725) Log-likelihood: -481.4



Call: glmmadmb(formula = ffSelfies ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation3.ri$residuals, center = TRUE) <= 2.96 & scale(nation3.ri$residuals, center = TRUE) >= -2.96, select = c(ffSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 965.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.171 0.249 12.74 < 2e-16 *** devfactor 1.870 0.315 5.93 3.1e-09 *** GIFac.womenstats 0.291 0.277 1.05 0.29 ZGINI 0.353 0.239 1.48 0.14 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=107, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.535 0.7314 Negative binomial dispersion parameter: 0.50642 (std. err.: 0.072166) Log-likelihood: -476.534



Call: glmmadmb(formula = ffSelfies ~ devfactor + GIFac.womenstats + ZGINI + (1 | Unmicroregion) + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation, scale(nation4.ri$residuals, center = TRUE) <= 2.96 & scale(nation4.ri$residuals, center = TRUE) >= -2.96, select = c(ffSelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 963.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.1996 0.3136 10.20 < 2e-16 *** devfactor 1.8143 0.3076 5.90 3.7e-09 *** GIFac.womenstats -0.0323 0.2941 -0.11 0.912 ZGINI 0.6458 0.2649 2.44 0.015 * devfactor:ZGINI 0.6529 0.2705 2.41 0.016 * devfactor:GIFac.womenstats -0.3086 0.2634 -1.17 0.241 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=107, Unmicroregion=20 Random effect variance(s): Group=Unmicroregion Variance StdDev (Intercept) 0.4503 0.6711 Negative binomial dispersion parameter: 0.52934 (std. err.: 0.0762) Log-likelihood: -473.672


Models with Restrictions on City Size

In our main analyses, we excluded cities with populations <5K inhabitants and counties

with populations <20K inhabitants. We reasoned that these areas may have too few social media

users (either due to small population, low urbanization and hence low internet connectivity, or

both), and their inclusion in the dataset could thus bias our results (as zero counts would not

reflect low sexualization, but low population). To check that the <5K cutoff was not too liberal,

we re-ran city analyses excluding cities with populations <20K (leaving n = 1,714 cities; 30% of

the data). 95% of remaining cities were comprised of >95% urban areas: Thus, in each city, we

can assume that at least 17K people lived in internet accessible urban areas. Like all of our other

analyses, these models offset the number of sexy selfies in an area by that area’s population, thus

accounting for the potential that zero counts were due to smaller populations. As shown on the

pages following, findings from these restricted analyses replicated those in the original

submission.


City Model 1 with Cities>20K

Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income | cityState) + offset(log(cityPopulation)), data = subset(city2, scale(city1zero.rirs$residuals, center = TRUE) <= 2.96 & scale(city1zero.rirs$residuals, center = TRUE) >= -2.96), family = "nbinom") AIC: 4486.4 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.8046 0.0876 -123.27 <2e-16 *** zcityGI.health 0.0980 0.1585 0.62 0.537 zcityGI.reproductive 0.2351 0.1353 1.74 0.082 . zcityGI.college 0.0268 0.1301 0.21 0.837 zcityGI.managerial 0.1847 0.1407 1.31 0.189 zcityGI.income -0.2948 0.1168 -2.52 0.012 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1636, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.10609 0.3257 zcityGI.health 0.28229 0.5313 zcityGI.reproductive 0.09018 0.3003 zcityGI.college 0.16072 0.4009 zcityGI.managerial 0.07986 0.2826 zcityGI.income 0.03767 0.1941 Negative binomial dispersion parameter: 0.34051 (std. err.: 0.022897) Log-likelihood: -2230.2



Call: glmmadmb(formula = citySexyselfies ~ zcityGini + (zcityGini | cityState) + offset(log(cityPopulation)), data = subset(city2, scale(city2zero.rirs$residuals, center = TRUE) <= 2.96 & scale(city2zero.rirs$residuals, center = TRUE) >= -2.96), family = "nbinom") AIC: 4685.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.8632 0.1032 -105.26 < 2e-16 *** zcityGini 0.4525 0.0743 6.09 1.1e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1687, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.24695 0.4969 zcityGini 0.04973 0.2230 Negative binomial dispersion parameter: 0.3414 (std. err.: 0.021705) Log-likelihood: -2337.73



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + (zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini | cityState) + offset(log(cityPopulation)), data = subset(city2, scale(city3zero.rirs$residuals, center = TRUE) <= 2.96 & scale(city3zero.rirs$residuals, center = TRUE) >= -2.96), family = "nbinom") AIC: 4445.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.9670 0.0896 -122.36 < 2e-16 *** zcityGI.health 0.1477 0.1610 0.92 0.359 zcityGI.reproductive 0.2438 0.1385 1.76 0.078 . zcityGI.college 0.0394 0.1076 0.37 0.714 zcityGI.managerial 0.1703 0.1418 1.20 0.230 zcityGI.income -0.1674 0.0909 -1.84 0.065 . zcityGini 0.4431 0.0604 7.34 2.1e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Number of observations: total=1637, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 1.157e-01 0.3402058 zcityGI.health 3.330e-01 0.5770269 zcityGI.reproductive 1.553e-01 0.3941320 zcityGI.college 2.704e-02 0.1644293 zcityGI.managerial 1.484e-01 0.3852402 zcityGI.income 2.894e-06 0.0017012 zcityGini 1.256e-07 0.0003544 Negative binomial dispersion parameter: 0.36626 (std. err.: 0.02473) Log-likelihood: -2207.75



Call: glmmadmb(formula = citySexyselfies ~ zcityGI.health + zcityGI.reproductive + zcityGI.college + zcityGI.managerial + zcityGI.income + zcityGini + cityEduEmplEarnpca + zcityMedianagefemale + zcitySexratio + zcityUrbanization + (zcityGI.health + zcityGI.college + zcityGini | cityState) + offset(log(cityPopulation)), data = subset(city2, scale(city4zero.rirs2$residuals, center = TRUE) <= 2.96 & scale(city4zero.rirs2$residuals, center = TRUE) >= -2.96), family = "nbinom") AIC: 4294.3 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.0368 0.0915 -120.63 < 2e-16 *** zcityGI.health 0.0479 0.1482 0.32 0.747 zcityGI.reproductive 0.1141 0.1106 1.03 0.303 zcityGI.college 0.0642 0.1293 0.50 0.619 zcityGI.managerial 0.2757 0.1077 2.56 0.011 * zcityGI.income -0.1657 0.0863 -1.92 0.055 . zcityGini 0.3178 0.0713 4.46 8.3e-06 *** cityEduEmplEarnpca -0.0931 0.0784 -1.19 0.235 zcityMedianagefemale -0.4005 0.0835 -4.80 1.6e-06 *** zcitySexratio -0.2897 0.1195 -2.42 0.015 * zcityUrbanization -0.3206 0.1779 -1.80 0.072 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1627, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.07778 0.2789 zcityGI.health 0.25581 0.5058 zcityGI.college 0.02014 0.1419 zcityGini 0.02227 0.1492 Negative binomial dispersion parameter: 0.40851 (std. err.: 0.028567) Log-likelihood: -2131.16


Models with Urbanization then Income Inequality

In the main analyses, there was a strong association between sexy selfies and urbanization

at the county level. To see whether the Gini effect was robust when compared directly with

urbanization, we analyzed the relationship between income inequality and sexy selfies by

entering urbanization to the null model and then adding income inequality (at the US city and US

county level). The addition of income inequality significantly improved the AIC model fit, was

statistically significant, and was of a comparable effect size to the models in the main manuscript.


City Model with Urbanization then Income Inequality

Call: glmmadmb(formula = citySexyselfies ~ zcityUrbanization + zcityGini + (zcityGini + zcityUrbanization | cityState) + offset(log(cityPopulation)), data = na.omit(subset(city, scale(cityurb.rirs$residuals, center = TRUE) <= 2.96 & scale(cityurb.rirs$residuals, center = TRUE) >= -2.96, select = c(citySexyselfies, zcityGini, cityPopulation, cityState, zcityUrbanization))), family = "nbinom") AIC: 8658.2 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.6484 0.1016 -104.79 < 2e-16 *** zcityUrbanization 0.2410 0.1409 1.71 0.087 . zcityGini 0.3468 0.0475 7.31 2.8e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=5495, cityState=51 Random effect variance(s): Group=cityState Variance StdDev (Intercept) 0.344800 0.58720 zcityGini 0.008992 0.09483 zcityUrbanization 0.386300 0.62153 Negative binomial dispersion parameter: 0.18897 (std. err.: 0.0093411) Log-likelihood: -4322.08


County Model with Urbanization then Income Inequality

Call: glmmadmb(formula = countySexyselfies ~ (1 | countyState) + zcountyUrbanization + zcountyGini + offset(log(countyPopulation)), data = na.omit(subset(county2, scale(countyurb.rife2$residuals, center = TRUE) <= 2.96 & scale(countyurb.rife2$residuals, center = TRUE) >= -2.96, select = c(countySexyselfies, zcountyGini, zcountyUrbanization, countyPopulation, countyState))), family = "nbinom") AIC: 3815.7 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.0865 0.1492 -81.03 < 2e-16 *** zcountyUrbanization 0.8588 0.0941 9.13 < 2e-16 *** zcountyGini 0.3600 0.0711 5.06 4.1e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=1553, countyState=50 Random effect variance(s): Group=countyState Variance StdDev (Intercept) 0.4847 0.6962 Negative binomial dispersion parameter: 0.34827 (std. err.: 0.024414) Log-likelihood: -1902.87


Models with only non-WEIRD countries

Most research on sexualization has been conducted in Western, educated, industrialized,

rich, demonstratic countries (WEIRD countries). To see if our results replicate in non-WEIRD

samples, we re-analyzed the data excluding WEIRD countries. We excluded Australia, New

Zealand, the UK, the USA, and all countries in Northern and Western Europe [59]. As shown in

the models following, the association between income inequality and sexualization in non-

WEIRD countries replicates that when WEIRD countries are included. Specifically, we find

support for there being a stronger association between income inequality and sexualization in

developed nations. We further find some evidence that gender inequality predicts sexualization in

under-developed nations.


Model 1 with only non-WEIRD countries

Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation2, scale(nation1nonweird.fe$residuals, center = TRUE) <= 2.96 & scale(nation1nonweird.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1090.8 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.546 0.160 34.61 <2e-16 *** devfactor 1.255 0.284 4.41 1e-05 *** GIFac.womenstats 0.334 0.250 1.34 0.18 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=86 Negative binomial dispersion parameter: 0.5402 (std. err.: 0.069485) Log-likelihood: -541.416


Call: glmmadmb(formula = sexySelfies ~ devfactor + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation2, scale(nation2nonweird.fe$residuals, center = TRUE) <= 2.96 & scale(nation2nonweird.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, ZGINI, devfactor, engpopfactorln, Unmicroregion))), family = "nbinom") AIC: 1108.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.468 0.156 34.95 <2e-16 *** devfactor 0.991 0.186 5.33 1e-07 *** ZGINI 0.192 0.143 1.34 0.18 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=88 Negative binomial dispersion parameter: 0.53861 (std. err.: 0.068506) Log-likelihood: -550.236



Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + ZGINI + offset(engpopfactorln), data = na.omit(subset(nation2, scale(nation3nonweird.fe$residuals, center = TRUE) <= 2.96 & scale(nation3nonweird.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1078.5 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.508 0.160 34.42 < 2e-16 *** devfactor 1.266 0.285 4.45 8.6e-06 *** GIFac.womenstats 0.297 0.244 1.22 0.22 ZGINI 0.209 0.146 1.43 0.15 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=85 Negative binomial dispersion parameter: 0.54481 (std. err.: 0.070571) Log-likelihood: -534.245


Call: glmmadmb(formula = sexySelfies ~ devfactor + GIFac.womenstats + ZGINI + devfactor:ZGINI + devfactor:GIFac.womenstats + offset(engpopfactorln), data = na.omit(subset(nation2, scale(nation4nonweird.fe$residuals, center = TRUE) <= 2.96 & scale(nation4nonweird.fe$residuals, center = TRUE) >= -2.96, select = c(sexySelfies, GIFac.womenstats, devfactor, engpopfactorln, Unmicroregion, ZGINI))), family = "nbinom") AIC: 1106.1 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 5.430 0.176 30.85 < 2e-16 *** devfactor 1.375 0.279 4.93 8.2e-07 *** GIFac.womenstats -0.171 0.263 -0.65 0.516 ZGINI 0.590 0.199 2.96 0.003 ** devfactor:ZGINI 0.560 0.262 2.14 0.032 * devfactor:GIFac.womenstats -0.557 0.280 -1.99 0.046 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of observations: total=87 Negative binomial dispersion parameter: 0.57502 (std. err.: 0.074078) Log-likelihood: -546.039


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