Virtual Life Satisfaction

Edward Castronova and Gert G. Wagner�

I. INTRODUCTION

Since2000, thenumberof users invirtual environments hasgrownrapidly, from

a few hundred thousand to many millions. At the same time, the definition of

‘virtual environment’ hasbecomebroader, as innovative socialmediaplatforms

increasingly provide arenas for human social interaction that are not quite

virtual worlds, but are more than mere websites. Facebook is an example. The

Farmvilleapplication inFacebookallowsuser tomake little farmsandchatwith

one another about their progress, sharing know-how, resources, and stories.

As of this writing, Farmville has more than 50m users. At the other end of the

spectrum is the massive 3D immersive environment World of Warcraft, with

more than 10m users paying $15 monthly to play with other people.

Some people spend so much time in virtual environments that they come

to think of it as their real or true place of residence (Castronova, 2001).

Anthropologists have begun to identify a blurring in the line between ‘real’ and

‘fantasy,’ in that the events in virtual environments seem to carry as much

emotional and social significance as offline events (Boellstorff, 2010). Thus, the

term ‘real life’ is increasinglyused inan ironic sense, referringonly to experiences

that occur offline, not to experiences that partake of more genuineness,

actuality, significance, or reality than those that occur online. ‘Real life’ and

‘virtual life’ are not different in meaning, only in the location of events. This

point of view is supported by evidence of a general equivalence of economic

behavior in real andvirtual environments (Castronovaetal., 2009a,Castronova

et al., 2009b). A number of similar studies find similar behaviors in other areas

(Bailenson et al., 2005, Yee and Bailenson, 2007, Reeves and Read, 2009).

KYKLOS, Vol. 64 – August 2011 – No. 3, 313–328

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� Castronova: Professor of Telecommunications and Cognitive Science, Indiana University, USA,

castro@indiana.edu. Wagner: Director, German Institute for Economic Research (DIW Berlin);

Research Fellow, CESifo, Munich, Germany; Professor, Berlin University of Technology (TUB),

Germany, gwagner@diw.de. We thank Mark W. Bell for research assistance and Wim Kalmijn for

help with the ‘Veenhoven-Kalmijn Transformation’. Part of this study was conducted in a virtual

environment created with funding from the German Federal Ministry of Education and Research,

Grant N0. 01UW0706 – PT-DLR.

The locations at which people direct their attention can have an effect on

their life satisfaction. Luigino Bruni and Luca Stanca used the World Values

Survey (as we do) to study the impact of TV viewing on life satisfaction (Bruni

and Stanca, 2006). Other research indicates that the characteristics of one’s

social environment have an effect on satisfaction. Importantly, it is not just

concrete material conditions thatmatter, but things like comparative incomes,

social relations, and the health of the world environment (Winkelmann and

Winkelmann, 2010, Becchetti et al., 2008, Berger, 2010). All of these things

might be viewed as ‘virtual’ rather than ‘literal’ contributions to well-being,

because they stem froman emotional stance about the state of theworld rather

than any immediate consumption of goods and services.

Thispaper followson this researchby treatingvirtual environments asquasi-

real places where life satisfaction can be higher or lower than it is in ‘real life.’

This framework generates new questions of fairly broad significance. First, if

virtual environments are places, they are places without telephones or home

addresses. How does one best conduct survey research within them? We have

addressed this set of questions in previous work, resulting in robust, industry-

standard methods for obtaining quasi-random samples of a virtual world

population (Bell et al., 2009, Bell et al., 2011).

More important, how do the conditions of life in these virtual places

compare to those in real places? In this paper, we report findings on this

question using a quasi-random sample of users of the virtual environment

SecondLife (hereafter SL). The SL users were asked a number of demographic

questions and about their sense of life satisfaction, both in SL itself and in their

‘first lives.’ We compare these data to findings from a much broader survey of

life satisfaction, the World Values Survey of 2005 (hereafter WVS).

Section 2 summarizes the known properties of life satisfaction data and best

practices for their interpretation and use. In Section 3 we compare the SL and

WVS samples to see whether results from the SL sample can be generalized.

In Section 4 we present life satisfaction results by country. Section 5 presents

life satisfaction by demographic category. Section 6 concludes.

II. LIFE SATISFACTION DATA

Life satisfaction data have become an accepted metric of well-being; social

scientists routinely use life satisfaction self-reports to compare well-being

across populations (Frey and Stutzer, 2010). Our basic method is to collect

life satisfaction self-reports from avatars in a virtual world and then compare

them to life satisfaction reports from the ‘real’ world.

For thevirtualworlddata,we collected surveydataon life satisfactionwithin

the virtual world of Second Life or ‘SL’ by means of ‘survey kiosks’ which we

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EDWARD CASTRONOVA/GERT G. WAGNER

dropped on different geographic locations in SL (which are called ‘islands’).

As it is discussed byBell et al. (Bell et al., 2009, Bell et al., 2011), our survey had

both quasi-random and convenience sampling protocols, the latter is common

in psychological research. In our case we employed three ways of reaching

participants: Mailing lists, advertisements, and the placement of survey kiosks

in random geographic locations.

The questionnaire of our survey is documented in (Bell et al., 2009). All

general questions were fully comparable to the questionnaire of the German

Socio-Economic Panel Study (SOEP) which is one of the most analyzed

surveys worldwide (Headey et al., 2010). From the SOEP’s point of view our

survey in SL is a ‘related study’ to the SOEP (Siedler et al., 2009). Some

questions were about the characters or ‘avatars’ within SL, but the life

satisfaction data we collected were only about the person using the avatar

and not the avatar itself.

The central survey question whichwe analyze is the one on satisfaction with

life. Following the literature (Schimmack et al., 2008) we ask ‘All in all, how

satisfied you are currently with your life in general’ (within SL and in the real

world). The respondents must respond by means of a scale which runs from

zero (5not satisfiedat all) and10 (5 fully satisfied).The scalehas 11points and

thus a midpoint (5 5).

This eleven-point-scale is considered to be the most valid and reliable scale

which can be used to measure satisfaction with life and certain life domains

(Schimmack et al., 2010). In fact this scale is applied in major surveys besides

SOEP as the European Social Survey (ESS), the Australian panel study

HILDA and the ‘Gallup World Poll’ (Layard et al., 2008, Kahneman and

Deaton, 2010).

For our real-world data, we sought data sources that used the same sort of

question as we used in the SOEP, and that had data for the countries whose

residents were most frequently present in SL. Among the possible surveys, the

Gallup data are not generally available for researchers, however, and the ESS

does not cover all the real-world countries most frequent in SL (especially

Brazil, Canada, and the USA). The World Value Survey (WVS), however,

contains comparable life satisfaction data for all of our target countries and is

freely available for research (see www.worldvaluessurvey.org and the ‘World

Database of Happiness’ at (http://worlddatabaseofhappiness.eur.nl/).1

One issue with theWVS data is that it does not use the 11-point scale of life

satisfaction (0 to 10), but rather a 10-point scale (1 to 10). Converting scales,

however, is an issue commonly addressed in the life satisfaction literature. It is

possible tomake responses to 10-point surveys comparable to 11-point surveys

1. WORLD VALUES SURVEY 2005 OFFICIAL DATA FILE v.20090901, 2009. World Values

Survey Association (www.worldvaluessurvey.org). Aggregate File Producer: ASEP/JDS, Madrid.

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by means of a transformation proposed by Kalmijn and co-authors (Kalmijn

et al., 2010) (the ‘KAV transformation’). TheKAV transformation, in essence,

replaces a rating j with an interval (j 2 1/2, j 1 1/2] around it, resulting in a

[1 2 1/2, 10 1 1/2] scale. Then this scale is given a downward translation over

adistanceof1/2, resulting ina [0, 10] scale.The results in this paper for theWVS

refer to KAV-transformed data. This allows full comparability between SL

data and the WVS data.

III. COMPARISON OF SAMPLES: WORLD VALUES SURVEY

AND SECOND LIFE

First we compare the demographic characteristics and mechanisms of life

satisfaction in the two samples we have, one from the real world and one

from the virtual world. In order to make meaningful comparisons, we limit

the sample to individuals from countries present in both the SL data and the

WVS data. Those countries are France, UK, Germany, Italy, Netherlands,

Spain, Canada, Brazil, and the US. Both samples make use of weights. In the

WVS sample, weights account for sampling probabilities in each country. In

the Second Life survey, we weighted respondents from different countries

so that their weight in the Second Life data would equal their weight in the

WVS data.

A common stereotype holds that video games are typically playedmostly by

young men and teenage boys. Second Life, as an online virtual environment,

might be expected to meet this stereotype. However, as seen in Table 1, the SL

data reveal a population not dramatically different from theWVS population

in terms of gender: 45 percent male in SL vs. 48 percent male in the WVS.

Nonetheless, Second Life users are by far younger, more likely to be students,

and more likely to be in poor health.

Next, we explored whether the sources of life satisfaction are about the

same in the two populations. As discussed in Section 2 above, life satisfaction

data seems to show robust patterns across countries and real-life demographic

samples. To check for similar results here,we ran regressions of life satisfaction

on demographic characteristics in the two samples (results not shown;

available from the authors). Patterns of magnitude and statistical significance

were the same in both samples. Thus, being unemployed or in poor health

has a large and statistically significant negative effect on life satisfaction in

both samples. Gender and age effects are not as prominent. This pattern is

roughly the same as has been found in other life satisfaction studies. We

conclude that Second Life ‘residents’ receive their life satisfaction in roughly

the same way as everyone else.

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EDWARD CASTRONOVA/GERT G. WAGNER

IV. LIFE SATISFACTION IN THEWORLD VALUES SURVEY AND

IN SECOND LIFE

Having determined that the SL sample and theWVS sample are demographi-

cally different, yet comparable in terms of the sources of life satisfaction, we

proceed to examine patterns of life satisfaction in each. Table 2 reports life

satisfaction data from theWVS. In theWVS data, a 1–10 scale has been used,

which does not directly compare to data from SL in that the SL data are based

on a 0–10 scale. As discussed in Section 2, this is a fairly common issue in life

satisfaction research that the lower anchor can be different in different studies.

SoKalmijn et al. offer an adjustment from the 1–10 scale to the 0–10 scale and

we have applied the adjustment to the WVS data in Table 2.

The table indicates that in the real world, average life satisfaction in a

country generally ranges from 6.5 to 7.5, with standard deviations ranging

from 1.5 to 2. For individuals, life satisfaction seems to range from 4 to 9.

In Table 3, we show how respondents in the Second Life sample rate their

first life and their Second Life. Column 1 of the table shows the first life

satisfaction, by country, while column 2 gives the SecondLife satisfaction. The

third column gives the difference in life satisfaction from real to virtual.

Table 1

Characteristics of Respondents

Characteristic World Values SurveyN 5 12,137

Second Life SurveyN 5 1,612

SexMale 0.48 0.45Female 0.52 0.55

Age18–25 0.14 0.3926–35 0.18 0.2936–45 0.21 0.1646–55 0.17 0.1156 1 0.30 0.05

EmploymentStudent 0.04 0.27Self-Employed 0.09 0.14Employed 0.47 0.37Unemployed 0.08 0.12Non-Employed 0.32 0.09

HealthVery Good 0.29 0.37Good 0.47 0.37Fair 0.20 0.19Poor 0.04 0.07

Weightedmeans.Sources:WorldValues Survey, 2005. IndianaUniversity / SOEP Survey of SecondLife, 2009.

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Table 2

Life Satisfaction in the General Population, by Country

Country Life Satisfaction

Mean Standard Deviation Observations

USA 6.82 1.74 1241Germany 6.63 1.89 2050

West 6.89 1.69 980East 6.38 2.03 1070

France 6.41 1.92 1000United Kingdom 7.09 1.63 1008Netherlands 7.24 1.35 1001Spain 6.82 1.52 1195Brazil 7.15 2.12 1495Canada 7.26 1.69 2141Italy 6.38 1.76 1006All Countries 6.89 1.80 12137

Source:WorldValues Survey, 2005. 1–10 scale used forLife Satisfaction.WeightedMeans.Adjustedto 0–10 scale using the Veenhoven-Kalmijn procedure (Kalmijn et al., 2010).

Table 3

Life Satisfaction in First Life and Second Life, by Country

Country(Observations)

Life Satisfaction

First LifeMean

(St. Dev.)

Second LifeMean

(St. Dev.)

Difference(2)–(1)

USA 7.34 7.62 0.28(819) (2.07) (1.88) ( 2 0.19)Germany 6.97 7.38 0.41(139) (2.07) (1.99) ( 2 0.08)France 7.04 7.44 0.40(113) (1.95) (1.73) ( 2 0.22)United Kingdom 6.88 7.50 0.62(182) (2.36) (1.88) ( 2 0.48)Netherlands 6.63 7.17 0.54(54) (1.71) (1.73) (0.02)Spain 7.24 7.98 0.74(58) (2.19) (2.18) ( 2 0.01)Brazil 7.13 7.69 0.56(45) (2.05) (1.81) ( 2 0.24)Canada 7.43 7.81 0.38(116) (2.06) (1.76) ( 2 0.30)Italy 7.16 7.68 0.52(50) (2.31) (1.72) ( 2 0.59)All Countries 7.30 7.61 0.31(1576) (2.08) (1.87) ( 2 0.21)

Source: IndianaUniversity / SOEPSurveyof SecondLife, 2009. 0–10 scale used forLifeSatisfaction.Weighted means.

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EDWARD CASTRONOVA/GERT G. WAGNER

Comparing Tables 2 and 3, it is clear that the distribution of real life

satisfaction by country is different in the SL sample than in the population at

large. SL users generally rate their real life satisfaction as higher than the

general population.

Regardless of country, however, the reported real life satisfaction of these

SecondLife users is lower than their satisfactionwith virtual life.Thedifference

seems to be very large in terms of general significance. For Germans, for

example, the satisfaction difference between virtual and real life is 0.41: average

life satisfaction in Second Life is 7.38, in real life, only 6.97.

To get a sense of the substantive meaning of these magnitudes, consider the

following comparison. Table 2 reports the life satisfaction of western

Germans and eastern Germans (those now living in the territory of the former

GermanDemocratic Republic). Until 1989, these two populations lived under

very different socio-economic conditions. The two regions of Germany

continue to be affected by this history, with the eastern part experiencing

higher ratesofunemployment andcertainkindsof crimeandalcoholism. In the

WVS, life satisfaction is lower among eastern Germans; western German life

satisfaction (6.89) is 0.51 points higher than eastern German life satisfaction

(6.38).Note, however, that this difference is only a bit larger than the difference

in German life satisfaction between real and virtual life. It suggests that an

eastern German could become almost as happy by entering Second Life as by

moving towesternGermany.Moreover, it ismuchcheaper and faster to ‘move’

into virtual reality than tomove in real space. Thus, life satisfaction differences

may be amajor reasonwhy some people use virtual environments so intensely.

In any case, the comparison to east-west satisfaction differences in Germany

indicates that the SecondLife –RealLife satisfaction differences are quite large

in substantive terms.

V. LIFE SATISFACTION AND INDIVIDUAL CHARACTERISTICS

Now we turn to the Second Life sample exclusively and explore how real life

satisfaction and virtual life satisfaction relate to the characteristics of indivi-

duals. We include standard characteristics in the study, such as country,

gender, age, and so on. We also include self-reported indicators of life

conditions, however, such as personal feelings, and judgments of activity in

daily life.

Wepresent four sets of results. First,we regress real life satisfactionon the set

of characteristics. Next, we regress Second Life satisfaction on the same

characteristics. Third, we regress the difference in life satisfaction on these

characteristics. All of these are OLS regressions (using weights to make the SL

sample by country comparable to the WVS).

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Finally, we test whether life satisfaction predicts Second Life use (as

suggested in Section 4) with a logit regression of intense Second Life use

(41–99 hours per week) on life satisfaction and the standard characteristics.

Table 4 reports the regression of real life satisfaction on characteristics.

Generally, country dummies seem to have no effect, except for Canada; for

some reason, being a Canadian SecondLifermakes you extraordinarily happy

with your first life. Gender, meanwhile, has no effect. Younger people have

higher life satisfaction, while those who are unemployed or out of the labor

force entirely (‘nonemployed’) are significantly less satisfied. Income has no

effect on life satisfaction, which is not surprising in light of ongoing research

(and thousands of art works) suggesting a low effect of money on happiness

(Frey and Stutzer, 2010). Finally, in terms of the standard demographics,

health has a huge effect on life satisfaction. As argued in Section 3, these

patterns are standard in the literature on life satisfaction. Employment and

health are major factors; youth is good; gender and money, not so important.

Table 4

Regression of Life Satisfaction on Individual CharacteristicsDependent Variable: Life Satisfaction

Variable Coefficient t-stat Variable Coefficient t-stat

USA 0.274 1.61 Angry 2 0.311 2 1.59Germany 0.151 0.64 Not Angry 0.203 1.62France 0.368 1.55 Worried 2 0.115 2 0.74Netherlands 2 0.119 2 0.46 Not Worried 0.158 1.15Spain 0.361 1.17 Happy 0.376 2.71Brazil 0.050 0.16 Not Happy 2 0.073 2 0.37Canada 0.797 3.27 Sad 2 0.658 2 3.41Italy 0.190 0.50 Not Sad 0.155 1.22Female 2 0.087 2 0.80 Rushed 2 0.495 2 3.65Age 18–25 0.640 3.40 Not Rushed 2 0.099 2 0.74Age 26–35 0.485 2.72 Fun 0.362 2.48Age 36–45 0.173 0.92 No Fun 2 0.215 2 0.56Age 56 and up 0.173 0.70 Interesting 0.296 1.95Student 0.014 0.09 No Interesting 0.711 2.04Self-Employed 2 0.076 2 0.44 Creative 2 0.034 2 0.25Unemployed 2 0.817 2 3.80 No Creative 0.440 1.35Nonemployed 2 0.454 2 2.22 Independent 0.078 0.61Income $0–10k 2 0.079 2 0.14 No Independent 0.173 0.53Income $10–20k 2 0.099 2 0.17 Meaningful 0.478 3.19Income $20–50k 2 0.079 2 0.13 NoMeaningful 0.011 0.03Income $50–75k 0.187 0.32 Useful 0.445 2.96Income $75–100k 0.008 0.01 No Useful 2 0.227 2 0.59Income $100–150k 2 0.125 2 0.20 Constant 5.856 8.63Income $200k 1 0.409 0.58Health Very Good 0.600 3.41Health Good 0.059 0.37Health Poor 2 0.620 2 2.01

Indiana University / SOEP Survey of Second Life, 2009. N 5 1469.

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EDWARD CASTRONOVA/GERT G. WAGNER

The second set of variables are not standard in the literature but involve self-

reported data in the SL sample regarding life conditions. Respondents were

asked to indicate whether they had had certain feelings recently, including

anger, worry, happiness, sadness, or feeling rushed. A Likert-type scale was

used for these questions. The regression variables are dummies using the

extreme positive and negative responses. Thus, ‘Angry’ indicates that the

respondent is Strongly Agrees or Agrees that he has had an angry feeling

recently. ‘Not Angry’ indicates Disagree or Strongly Disagree. The middle

category is omitted.

Comfortingly (for the maintained assumptions of life satisfaction work),

feeling happy has a strong positive impact on life satisfaction, while feeling sad

has the opposite effect.2 Feeling angry or worried is less significant, though

feeling rushed is quite impactful.

The final set of variables, also about life conditions, ask about recent events

rather than feelings. Respondents are asked whether they have, in the past

week, engaged in activities that are fun, interesting, creative, independent,

meaningful, or useful.Again aLikert-type scalewas used anddummies created

using the Strong Agree/Agree responses and the Disagree/Strong Disagree

responses. We see that people who engage in fun, interesting, meaningful, and

useful activities experience significantly higher life satisfaction, while creative

or independent activities are not as impactful. A certain recipe for life satis-

faction emerges from the results which, again, are corroborated by other life

satisfaction studies: Watch your health, keep a job (or stay in school); and

schedule your time so you are not rushed; and finally, try to do lots of fun,

interesting, meaningful, and useful things. You also should try not to grow

older, though most people find that task quite challenging.

Next, in Table 5, the question is whether satisfaction with Second Life

follows similar patterns.Who gets themost satisfaction from their virtual life?3

In terms of the usual demographics, the results here are far less clear than with

real life satisfaction. There does not seem to be any pattern that relates real-life

observable characteristics such as age, gender, employment, or income to

satisfaction with virtual life. The only statistically significant result says that

very good health predicts high satisfaction with Second Life too. This is

counter-intuitive; onemight rather think that primarily people with low health

2. There is an operational difference between life satisfaction and feelings of happiness and sadness. The

feelingsare self-reportsof recent emotions.Life satisfaction is anoverall assessmentof long-run status.

Though likely to be related, the two concepts are not the same. We feel sad when a child leaves home

for school, but we can be quite satisfied with that outcome.

3. A clarification: In Section 3wediscussed comparing theWVSsample and the SL sample by regressing

real life satisfaction on demographic characteristics in both samples. That exercise is different from

whatweare doinghere.Here,weareusing theSLsampleonly.Within that sample,wefirst regress real

life satisfaction on demographics, and then we regress Second Life satisfaction similarly.

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and associated challenges with daily life with find their time in a virtual

environment much more satisfying.

Within the self-reported categories, some more intuitive results are found.

Being Not Happy increases satisfaction with virtual life. Being Rushed

decreases it. Apparently, we no more enjoy virtual stress than real. Finally,

respondentswhosay theyhavedone somethingFunorCreative in thepastweek

also scorehigheronvirtual life satisfaction.This seems tieddirectly to thenature

of the virtual environment itself, which is fun and creative by design. Respon-

dents who are using Second Life in that way are naturally satisfied with it.

Onthewhole, however, theseareonlyhintsofwhatmight induce satisfaction

with a virtual life. More research should be done on this specific question.

In Table 6 we regress the difference in life satisfaction on this same set of

regressors. The thought experiment here is, who gains the most from ‘switch-

ing’ from real to virtual? And how does this potential switch compare to

changes one might pursue in real life? As we saw above, there was some

Table 5

Regression of Second Life Satisfaction on Individual CharacteristicsDependent Variable: Second Life Satisfaction

Variable Coefficient t-stat Variable Coefficient t-stat

USA 0.074 0.46 Angry 0.185 0.92Germany 2 0.408 2 1.78 Not Angry 0.183 1.45France 0.201 0.90 Worried 2 0.127 2 0.84Netherlands 2 0.090 2 0.36 Not Worried 2 0.128 2 0.86Spain 0.262 0.77 Happy 0.243 1.66Brazil 0.029 0.10 Not Happy 0.448 2.32Canada 0.249 1.09 Sad 2 0.034 2 0.19Italy 0.117 0.39 Not Sad 2 0.165 2 1.29Female 0.035 0.30 Rushed 2 0.344 2 2.38Age 18–25 2 0.010 2 0.05 Not Rushed 0.176 1.30Age 26–35 0.070 0.41 Fun 0.524 3.67Age 36–45 0.080 0.43 No Fun 0.403 1.20Age 56 and up 2 0.015 2 0.06 Interesting 0.264 1.91Student 0.074 0.46 No Interesting 2 0.205 2 0.49Self-Employed 0.066 0.39 Creative 0.360 2.59Unemployed 2 0.215 2 1.09 No Creative 0.452 1.44Nonemployed 2 0.357 2 1.57 Independent 0.063 0.45Income $0–10k 2 0.109 2 0.19 No Independent 0.153 0.49Income $10–20k 2 0.376 2 0.65 Meaningful 0.050 0.33Income $20–50k 2 0.581 2 1.00 NoMeaningful 2 0.352 2 0.96Income $50–75k 2 0.534 2 0.92 Useful 2 0.188 2 1.35Income $75–100k 2 0.595 2 1.02 No Useful 0.533 1.65Income $100–150k 2 0.207 2 0.33 Constant 7.039 11.05Income $200k 1 0.391 0.64Health Very Good 0.359 2.02Health Good 0.061 0.38Health Poor 0.093 0.31

Indiana University / SOEP Survey of Second Life, 2009. N 5 1469.

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likelihood that an eastern German who went into Second Life might increase

her life satisfaction almost as much as she would by becoming a western

German. Do such effect-size magnitudes (in the German case, on the order of

0.5) remain when other life conditions are taken into account?

The numbers suggest that young people seem to experience a smaller gap

between real and virtual life satisfaction. This may not be a surprise. Consider

students, for example.Onemight consider the life of a student asavirtualworld

in its own right, in which the maturing person is sheltered from painful effects

and work burdens, while being allowed to play-act positions of responsibility

(suchas editor of the student paper, orpresident of student government). If this

is so, then the virtual world of Second Lifemay not seem all that different from

that of ‘Student Life.’

Those who are unemployed, Angry, and Sad/Not Happy seem to gain

considerably from the transition to Second Life from real life. The effects sizes

Table 6

Regression of Difference between Second Life Satisfaction and Life Satisfaction on IndividualCharacteristics

Dependent Variable: SATDIF 5 Second Life Satisfaction – Life Satisfaction

Variable Coefficient t-stat Variable Coefficient t-stat

USA 2 0.203 2 0.97 Angry 0.502 2.09Germany 2 0.561 2 1.89 Not Angry 2 0.020 2 0.13France 2 0.220 2 0.74 Worried 2 0.025 2 0.14Netherlands 0.040 0.12 Not Worried 2 0.338 2 1.93Spain 2 0.115 2 0.33 Happy 2 0.093 2 0.54Brazil 0.010 0.02 Not Happy 0.585 2.48Canada 2 0.551 2 1.86 Sad 0.661 2.89Italy 2 0.064 2 0.18 Not Sad 2 0.277 2 1.79Female 0.132 0.97 Rushed 0.142 0.81Age 18–25 2 0.684 2 2.90 Not Rushed 0.290 1.76Age 26–35 2 0.419 2 1.93 Fun 0.219 1.21Age 36–45 2 0.057 2 0.24 No Fun 0.638 1.43Age 56 and up 2 0.231 2 0.70 Interesting 2 0.066 2 0.35Student 0.079 0.39 No Interesting 2 0.924 2 1.71Self-Employed 0.115 0.52 Creative 0.393 2.30Unemployed 0.617 2.35 No Creative 0.011 0.03Nonemployed 0.093 0.35 Independent 2 0.003 2 0.02Income $0–10k 2 0.119 2 0.31 No Independent 2 0.078 2 0.20Income $10–20k 2 0.306 2 0.78 Meaningful 2 0.405 2 2.17Income $20–50k 2 0.536 2 1.34 NoMeaningful 2 0.342 2 0.69Income $50–75k 2 0.803 2 1.97 Useful 2 0.629 2 3.44Income $75–100k 2 0.699 2 1.63 No Useful 0.755 1.73Income $100–150k 2 0.130 2 0.25 Constant 1.195 2.27Income $200k 1 2 0.214 2 0.35Health Very Good 2 0.284 2 1.34Health Good 2 0.005 2 0.03Health Poor 0.659 1.73

Indiana University / SOEP Survey of Second Life, 2009. N 5 1469.

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here are just as largeas the simple comparisoneffects sizes; on theorderof 0.5or

more. This leads to some interesting speculations. An unemployed personwho

enters Second Life gains 0.617 in life satisfaction. According to Table 4, an

unemployed person who became employed would gain 0.817 in life satisfac-

tion.4 Again, given that ‘moving’ to Second Life involves little more than a

computer and an internet connection (and free time, which the unemployed

have in abundance), the comparable effect sizes here suggest that some people

may be strongly motivated to take refuge in a virtual life rather than try to

change their real life.

Other statistically significant effects suggest that having a moderate income,

anddoingcreative,useful, andmeaningfulacts,havesomeeffectonthe likelihood

of gaining or losing life satisfaction from making the move to the virtual.

Finally, inTable 7we exploremoredirectly the idea that life satisfactionmay

induce a behavioral change. Using data on hours devoted to Second Life, we

break the sample into intense users (41–99 hours per week) and mild users (all

others). We then regress the dummy variable ‘41–99 hours per week’ on all of

the above characteristics, as well as real life satisfaction.

Holding all characteristics constant, having a higher life satisfaction reduces

the odds of being an intense Second Life User lower. In terms of substantive

significance,when life satisfaction goesupby1point, theprobability of the left-

hand side event is changed in relative terms by exp(2 0.14) or 0.87. In other

words, a point of life satisfaction makes the event [Use Second Life Intensely]

13 percent less likely to occur. By way of comparison, the effect of being 18–25

(as opposed to 46–55, the omitted category) is 0.38, reducing the relative

probability by 62 percent. In even larger magnitudes, the poor and the rich are

quite likely to be intense users. Other variables generally do not indicate a

strongpattern. It is interesting that life satisfactionhas an effect butHappiness/

Sadness does not.

VI. CONCLUSION

In this paper we have explored patterns of life satisfaction in the real world and

in a virtual world (SecondLife).We find that themechanisms that generate life

satisfaction are the same for virtual world users and for the population at large.

However, the forces that produce virtual life satisfaction seem different from

those that produce real life satisfaction. Better data on the characteristics of

virtual life would be needed to explain this difference. For example: We have

4. In Table 4, the category ‘Employed’ is the omitted category. Thus the coefficient 2 0.817 on the

Unemployed variable indicates the difference in life satisfaction from beingUnemployed rather than

Employed. Were the unemployed person to become employed, all else equal, he would gain 0.817

points of life satisfaction.

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EDWARD CASTRONOVA/GERT G. WAGNER

data on unemployment status in the real world. What is the equivalent in a

virtual world? Towhat extent do people in a virtual world experience the sense

of devaluation that attends unemployment in real life? Do people in virtual

worlds feel bored, unuseful, undemanded at times? Does this fluctuate over

time, and would it make sense to track a ‘virtual world unemployment’

concept? Perhaps if such a statistic were recorded, it too would indicate a

strong correlation with measures of virtual life satisfaction. A similar case

might bemade for ‘virtual health’ – if theperson’s character in the virtualworld

is physically limited or constrained in some way, this might have the same

impact on virtual life satisfaction as poor health has on real life satisfaction.

The effect of virtual environments on life satisfaction and behavior seems to

be rather significant. Life satisfaction in virtual environment is certainly large

enough to present a viable alternative to real life for some individuals. In most

cases in these data, Second Life satisfaction exceeds first life satisfaction. This

Table 7

Logit regression of heavy Second Life use on Life Satisfaction and Individual CharacteristicsDependent Variable: SLHRS4199 5 Spend more than 40 hours in Second Life each week

Variable Coefficient t-stat Variable Coefficient t-stat

Life Satisfaction (0–10) 2 0.142 2 2.06USA 2 0.514 2 1.65 Angry 0.170 0.42Germany 2 0.636 2 1.37 Not Angry 0.099 0.36France 2 0.477 2 0.90 Worried 2 0.099 2 0.30Netherlands 0.025 0.05 Not Worried 0.106 0.33Spain 2 0.112 2 0.21 Happy 2 0.145 2 0.47Brazil 2 0.030 2 0.05 Not Happy 2 0.351 2 0.85Canada 2 1.160 2 2.05 Sad 2 0.580 2 1.53Italy 2 0.159 2 0.26 Not Sad 2 0.138 2 0.46Female 0.426 1.73 Rushed 0.099 0.31Age 18–25 2 0.945 2 2.35 Not Rushed 0.235 0.76Age 26–35 2 0.978 2 2.53 Fun 0.616 2.09Age 36–45 2 0.031 2 0.08 No Fun 0.241 0.33Age 56 and up 0.119 0.23 Interesting 0.186 0.60Student 2 0.154 2 0.42 No Interesting 0.029 0.04Self-Employed 2 0.285 2 0.61 Creative 0.273 0.95Unemployed 0.593 1.61 No Creative 0.785 1.61Nonemployed 0.672 1.91 Independent 0.236 0.98Income $0–10k 1.956 2.03 No Independent 2 0.407 2 0.65Income $10–20k 1.483 1.48 Meaningful 2 0.130 2 0.47Income $20–50k 1.368 1.37 NoMeaningful 2 0.453 2 0.64Income $50–75k 0.857 0.83 Useful 2 0.348 2 1.14Income $75–100k 1.304 1.24 No Useful 2 0.490 2 0.74Income $100–150k 2.216 2.09 Constant 2 2.804 2 2.32Income $200k 1 2.935 2.49Health Very Good 0.148 0.42Health Good 2 0.014 2 0.04Health Poor 0.649 1.51

Data: Indiana University / SOEP Survey of Second Life, 2009. Weighted regression. N 5 1469.

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VIRTUAL LIFE SATISFACTION

stands to reason; the SecondLife sample is taken fromusers of a virtual world,

of course.

Perhaps the main significance of such a finding is to re-emphasize the

commonpoint thatwhenpeoplehave chosen something, it is almost always for

a good reason, at least from their perspective. Here we have users of a virtual

environment reporting (apparently honestly, since the patterns of their reports

donotdifferent significantly from thoseof thegeneral population) that the time

they spend in virtual reality offers them more satisfaction than the time they

spend out of it. This is like finding that people who own cars enjoy driving. It

stands to reason. Yet reason is often clouded when prickly issues generate

debates about policy. It is too easy to forget that the users of virtual

environments gain significantly from them. For these people, if not for others,

making the move to virtual reality has paid off.

It is striking, moreover, that the effect size for this simple move to a virtual

reality (VR) is comparable (evenafter accounting formanyother factors) to the

effect on life satisfaction ofmajor life changes, such as changing one’s health or

taking a job. While people in VRmay be making gains from their choice, one

mightmakea case for interventionagainst that choice if it seems to inhibit other

activities important for society, such as working or going to school. Alter-

natively, onemight use these results to criticize the outside world – perhaps the

problem here is not that some people are escaping into virtual worlds, but that

the real world is not providing enough life satisfaction. If the escape to the

virtual is, in the end, reasonable and based on a sensible assessment of where a

person can bemost happy, this only points a criticizing finger at reality.Why is

reality suffering fromthis comparison?Why isn’t real lifemore satisfying thana

fairly crude 3d graphical environment? Why are hundreds of millions of

eyeball-hours being spent in virtual reality? Has something gone wrong with

the real world?

We have partly addressed such questions by regressing a measure of time

spent in the virtual world on individual characteristics, finding that low life

satisfaction in real life is indeed a significant predictor of intense virtual world

use. Indeed it is one of the few measurable characteristics that offer a strong

direction; others seem to relate to peoplehavingmore free timeormoremoney.

More work needs to be done on why people decide to spend time in virtual

environments, but these results suggest, at leastmildly, that dissatisfactionwith

real life is an important part of their decision.

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SUMMARY

Westudy life satisfactiondata from the 2005WorldValues Survey anda 2009 surveyof users of the virtual

world SecondLife. SecondLife users donot have the samedemographic profile as the general population,

but the differences are not as large as we expected. Themechanisms and causes of life satisfaction seem to

be similar in the two samples. Among Second Life users, satisfaction with their virtual life is higher than

satisfaction with their real life. Regression analysis indicates that people in certain life situations, such as

unemployment, gain more life satisfaction from ‘switching’ to the virtual world than from changing their

real-life circumstances. Inotherwords, anunemployedperson canbecomehappier by visitingSecondLife

rather than finding a job. Correspondingly, problems in real life are positive predictors of intense use of

virtual life.

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EDWARD CASTRONOVA/GERT G. WAGNER