The Effects of Social Life, Economic Opportunities, Overall Climate,

and Financial Situation on Perceived Happiness

Rachel A. Didur

California State University, Fullerton







Participants consisting of 176 people completed an on-line experiment via the Internet.This study was intended to examine four factors of a city and their importance in contributing to the happiness of individuals.The four factors used for analysis were social life, employment opportunities, overall climate, and financial situation.Participants went on-line, read descriptions of the four factors portrayed as either good, unknown, or poor, and were asked to rate their perceived happiness of 81 trials when the three conditions of the factors were varied.Ratings of perceived happiness were based on a nine-point scale.Results indicated that bad things make everything else look bad and good things make everything else look good.†† These results were not consistent with the averaging model, which states that the effect of any piece of information is going to be inversely related to the weight of the other information.These results were, however, consistent with the hypothesis that when the factors were labeled as all good and all poor, individuals would exaggerate their perceived happiness, which gave us implications of how societies view happiness.









The Effects of Social Life, Economic Opportunities, Overall Climate,

and Financial Situation on Perceived Happiness

The American dream seems to have become life, liberty, and the purchase of happiness.Subjective well-being (SWB), used interchangeably with ďhappiness,Ē denotes a personís evaluation of his or her life.This evaluation is both cognitive and affective.Research of SWB has shown that happiness is composed of three related components: positive affect, absence of negative affect, and satisfaction with life as a whole (Lu, 1999).Lu (1999) reported that personal and environmental factors do predict happiness.He analyzed an integrative model of happiness, which incorporated personal factors and environmental factors, using a longitudinal data set in order to clarify the relationship between overall happiness and life satisfaction.Participants were given a structured questionnaire at two different times, separated by 2.5 years.The data included demographic information, personality traits, life events, social support, and SWB.When the SWB levels and personality traits were statistically partialled out, social support had an effect on overall happiness, and positive life events had a positive effect on life satisfaction.

††††††††††† Better clues of SWB come from knowing about a personís traits, close relationships, work experiences, culture, and religiosity (Myers & Diener, 1995)Recognizing that most people are reasonably happy, but that some people are happier than others, Myers and Diener (1995) explored the question, ďWho is Happy?Ē.They found that a personís age, sex, race, and income do not give clues for happiness.Better clues come from knowing a personís traits, whether the person enjoys a supportive network of close relationships, whether the personís culture offers positive interpretations for most daily events, whether the person is engaged by work and leisure, and whether the person has a faith that entails social support, purpose, and hope.By asking who is happy, and why, researchers try to help people rethink their priorities and better understand how to build a world that enhances human well-being (Myers & Diener, 1995).

††††††††††† As past research has shown, there are many factors that contribute to happiness.There are counterfactual thoughts about oneís own happiness that play an important role in human lives.People sometimes wonder about what it would be like to be in another job, in another city, or even with another spouse.This curiosity creates an effect described by Schkade and Kahneman (1998) as a focusing illusion.A focusing illusion is when a judgment about an entire object or category is made with attention focused on a single dimension.The attended dimension is overweighted relative to unattended dimensions.More specifically, when attention is drawn to the possibility of a change in any significant aspect of life, the perceived effect of this change on well-being is likely to be exaggerated.Schkade and Kahneman (1998) took large samples of students in the Midwest and in Southern California and had them rate satisfaction with life overall as well as with various aspects of life, for either themselves or someone similar to themselves.Self-reported overall life satisfaction was the same in both regions, but participants who rated a similar other expected Californians to be more satisfied than Midwesterners.Climate-related aspects were rated as more important for someone living in another region than for someone in oneís own region.Analyses also showed that satisfaction with climate and with cultural opportunities accounted for the higher overall life satisfaction predicted for Californians.Participants who made judgments of life satisfaction in a different region were susceptible to this focusing illusion.This illusion occurred because easily observed and distinctive differences between locations are given more weight in such judgments then they will have in reality.

††††††††††† The purpose of the present experiment was to replicate earlier experiments regarding happiness and life factors.Participants went online to complete a questionnaire and were asked to imagine living in different cities and rate how happy they would be if they lived in a city described by four factors that were listed.The four factors chosen to analyze were social life, employment opportunities, overall climate, and financial situation.These were the four factors chosen because, in the previous experiment, they were rated as varying widely in importance.

In the present study, it was predicted that a focusing illusion would take place.Namely, people will give more weight, therefore believe they will be more happy living in places that are described as having good qualities.It is also predicted that the factors that they find to be more important will make them believe they will be more happy in such a city.The predictions of the means were based on the theory, Zen of Weights.This theory, explained by the averaging model, predicts judgment of happiness based on the following forumula:




where WS, WE, WC, and WF are weights, or importances, of social life, employment opportunities, overall climate, and financial situation dimensions of the city.When information is unknown, its weight is assumed to be zero. Each of these dimensions of the city also has a scale value.This averaging model predicts that the weights and scales combine to give a prediction, when the amount of information is fixed.The effect of any piece of information is predicted to be inversely related to the weight of the other information.This model also predicts that there will not be any interactions; in essence, any two factors will have parallel effects on the dependent variable.However, the effect of any variable should be least when all four pieces of information are presented.



Participants read the instructions at the top of the experiment to themselves.The participants were informed that this study was being conducted to evaluate the importance of various factors to happiness.They were asked to imagine living in different cities and to rate how happy they would be if they lived in such a city.


††††††††††† A 3x3x3x3, Social life by Employment opportunities by Overall climate by Financial situation, factorial design was conducted in which the three levels of all four factors were either poor, unknown, or good.


††††††††††† The questionnaire consisted of 92 total questions.Trials were given to the participants that asked them to rate how happy they would be in a city with such conditions.Each trial was presented in the format of the following example:





††††††††††† Experimental manipulation of factors affecting happiness.The independent variable of the factors that affect happiness was manipulated by varying the degree to which four factors were assumed in any given city.The four factors were social life, employment opportunity, overall climate, and financial situation and were described as being poor, good, or unknown.In the social life condition, a city with a poor social life offered few opportunities to socialize because there werenít any places that held social gatherings.More specifically, there wasnít any easy way to meet people.A city described as having a good social life provides many places in the city to meet people including clubs, hobby groups, church socials, night clubs, etc.There are many social gatherings set up by the community in this condition.In the employment condition, a city with poor employment opportunities had many more people looking for jobs that there were jobs available to them.Basically, it is very hard to find good jobs.A city with good employment opportunities had more jobs than there were people looking for them, and it is easy to find work.In the climate condition, poor climate was described as being mostly miserable, whereas good climate was described as being mostly beautiful.The financial situation condition referred to the overall socioeconomic level of the community.A poor financial situation had more than a third of the population of the city falling below the poverty level.A good financial situation had less than 1% below the poverty level, and more than 90% of the community in the middle class or above.The unknown condition for each of the above factors had no information to give.

††††††††††† Dependent measures.After reading each condition, the participants were asked to rate how happy they would be in a city with the factors given.The participants were asked to rate how happy they believed they would be on a 9 point scale from (1)very very unhappy to (9)very very happy.Second, the participants were asked to rate their current happiness of the same four factors along with their overall happiness in their current living conditions using the same 9 point scale.The demographic questions included were age, gender, education, nationality, and residency.

††††††††††† Manipulation check.At the end of the questionnaire, participants were asked to rate on a scale from 0 to 100 how important each factor would be to their happiness of living in a city.


††††††††††† Participants were brought into a computer lab, sat in front of a computer, taught how to use the computer, and given a web site address to complete the on-line experiment.


††††††††††† Participants consisted of people who volunteered to complete this on-line experiment.Many participants received credit as one option of an assignment in their Psychology 101 class at California State University, Fullerton.Other participants were recruited via the Internet.They ranged from 18 to 59 years of age.


††††††††††† According to the figures described by the first figure caption, when all four factors were listed as poor, participants rated their subjective happiness to be very low.When factors were gradually changed from poor to unknown and then to good, participants subjective ratings of life happiness increased dramatically.Participants believed that they would be very unhappy (M = 1.59) if all the conditions labeled in the city they were living in were poor, and very happy (M = 8.03) if all the conditions were labeled as good.These means and the figures explain the way that Americans think in terms of life happiness.They seem to believe that the only way they will be very happy is if there are good things around them, as shown by the increase in the lines when the factors were all listed as good.

††††††††††† The figures described by the second figure caption show a divergence between the factors and each variation there of.As shown by these figures, each factor was rated differently.More specifically, participants gave the variables different weights.A repeated measures analysis of variance was run to further examine the ratings across the four factors and to view how great the difference was in their measurements.Each main effect, social life, economic opportunities, overall climate, and financial situation were significantly different from each other.More specifically, these groups all differed in how they were rated according to the condition they were given.Also, each factor, along with each factor in comparison to every possible variation of the other factors gave us significant F values where p < .01.There were also significant interactions between each variation of these factors, elucidating that all variables are different from one another and they diverge.The difference between the means was significant, F (16,2800) = 9.496, p < .01.The interaction between the four factors has the greatest significance level when social life and economic opportunities are given a linear fit, and when overall climate and financial situation are given a linear fit.As the figures described by the second figure caption show, these factors diverge due to the different fits that are associated with them.These variables were further examined by analyzing which factor contributed most happiness and unhappiness.The cityís factor that contributed most to both happiness (M = 5.104) and unhappiness (M = 3.254) was the financial situation.This brings me to the conclusion that people would chose not to live in a city that has a lot of poverty and more people flock to a city that has a large portion of upper class.This conclusion was drawn from the results of the repeated measures analysis, but our figures show somewhat different results.

By looking at the figures that display the marginal means for each variable alone and also each variable combined with the different variations of the other variables, we see the most distinct information displayed for us with overall climate.As predicted, when climate is presented alone, the line should be the steepest.As other information is presented, the line flattens out.In this figure, we see that when each other variable is presented alone with climate, the line flattens out.This tells us that each other variable carries a lot of weight.Though it is interesting to see each variable presented together; this variable is almost as steep as climate presented alone.We can therefore conclude that something else is going on.

††††††††††† The marginal means graphs for the employment opportunities, social life, and financial situation are a bit different.The line of the factor displayed alone and the line where all four factors are displayed together seem to be parallel in their steepness.These figures convey that this averaging model may not fit in our analysis because the relative weight seems to be equal.


††††††††††† The results supported the hypothesis that people believe they will be more happy living in places that are described as having good qualities.As seen from the results, people believed they would be very happy when the city they imagined living in had good conditions, and very unhappy when the city had poor conditions.In general, as conditions gradually went from good to poor, their subjective happiness also declined.The present results are consistent with Schkade and Kahneman (1998) who found that judgments of life satisfaction in a different location were inclined to a focusing illusion.Participants generally gave more weight to their judgments than they are likely to have in reality.

††††††††††† The results were not consistent with neither the additive model nor the relative weight averaging model, which predicted that the variables would not have interactions.In fact, each variable was found to interact with every possible combination of the other variables.When a variable was singled out and compared to every combination of it, the lines representing good and poor conditions tended to diverge.The present results may be explained by something else that was going on.It is possible that the four factors chosen, social life, economic opportunities, overall climate, and financial situation are weighted very difference in importance for happiness.As found from the results, the financial situation of a city can be the most attractive feature of a city or the most unattractive.In general, participants found that if the financial situation of a city was good, they could see themselves as being more happy.

††††††††††† A possible explanation for the present results may be based on what Myers and Diener (1995) believe is a viable theory of happiness.It is believed that the first component is to recognize the importance of adaptation.For example, lottery winners are initially elated, but that soon wears off.In addition to adaptation, a second component of a theory of happiness as described by Myers and Diener (1995) is cultural worldview.Many cultures view the world as in very different ways and they hold very different ideas of what makes them happy.A third component is values and goals.Thefour factors used to predict happiness can only be appropriate in prediction of a personís happiness if they were relevant to a personís goals.This theory reasons that people should attempt to resolve the discrepancy between what they believe will make them happy and what in actuality, will make them happy.

††††††††††† The present results also have implications for real world situations.For one, many peoples beliefs about what will make them happy may be exaggerated.People should think twice before moving to a city because it has better conditions.The present results seemed to show that there was something more going on in judgments of happiness than predicted by both the additive model and the averaging model.Society and the media seem to portray that happiness comes with good qualities, which is not always the case.These results also show us what is important to people in choosing where they want to live.It seems that overall climate and financial situation were very important to people, whereas social life and employment opportunities were not.This is very interesting because people tend to be more concerned with the aspects of the city rather than the factors that directly affect them.If a person does not have a good job nor many friends, but he or she lives in a city with good climate and not a lot of poverty, will that person still be happy?The present results say that a person thinks they will be happy with this type of life.

††††††††††† A question arises to the validity of the results obtained by this questionnaire.It may not be valid for a few reasons.One reason may be due to a personís individual circumstances that may have persuaded he or she to answer that they will be very happy with the conditions that were given regarding an imaginary city.For example, a person might feel that he or she would be more happy if the overall climate was good.If a particular individual has dreams and goals of becoming a lifeguard and needed warmer weather for it, that person might indeed feel that he or she would be happier.Another reason may be due to the factors given to base judgments of happiness on.It can be very difficult to base oneís subjective feelings of happiness on the conditions of a city.For many, the conditions of a city may have nothing to do with how happy they really are. Also the descriptions of the factors were so widely different from each other that it may have felt like an obvious question of happiness when something was described as extremely poor conditions versus extremely good conditions.Further research should examine the variables of more personal factors, such as oneís family circumstances.By including factors that are not only related to the conditions of a city, it may be more apparent what is really important to Americans.With additional research including a variety of different aspects of a personís life, generalizations of these results will be more feasible.

††††††††††† Thoughts about oneís own happiness play an important role in human lives.People often wonder about what it would be like to be in another city or in another job.If people act on these thoughts in the belief that they would be more happy, they can have serious consequences.Many individuals make erroneous predictions that life would be better in a place that has better conditions, for example, a better climate.In the topic of life happiness, the present discussion suggests that people may not be good judges of the effect of changing circumstances on their own life happiness.However, happiness may be due more to intrinsic qualities of the person than to the extrinsic qualities of the city.



††††††††††† Diener, E., & Diener, C.(1996).Most people are happy.Psychological Science, 7 (3), 181-185.

††††††††††† Lu, L. (1999).Personal or environmental causes happiness: A longitudinal analysis.The Journal of Social Psychology, 139 (1), 79-90.

††††††††††† Myers, D.G., & Diener, E.(1995).Who is happy?Psychological Science, 6, 10-17.

††††††††††† Schkade, D.A., & Kahneman, D.(1998).Does living in California make people happy? A focusing illusion in judgments of life satisfaction.Psychological Science, 9 (5), 340-345.














Figure Captions

Figures 1.The four figures on the first page of figures graphs the mean judgment of social life, employment opportunities, overall climate, and financial situation.The lines represent both good and poor social life against financial situation, where the overall climate and employment opportunities are displayed as being either good or poor.As shown, when everything is labeled as poor, individuals have a low perception of their life happiness.As things are gradually labeled as good, individuals believe they will be happier.When every factor is labeled as good, when climate=good and employment=good, the lines have a greater separation between them and begin to diverge.More specifically, the more things labeled as good, the bigger effect of social life.

Figures 2.The four figures on the second page of figures graphs the marginal mean judgment of social life, employment opportunities, overall climate, and financial situation.These factors are each graphed independently and graphed with every possible combination of the other factors.According to the averaging model, the line of the factor graphed alone is supposed to be the steepest line and the line of the factor graphed with the other three factors is supposed to be the flattest.As the factors are added, the line is supposed to gradually flatten, though that is not what is seen.In many cases the line with all four factors is parallel or even steeper than the line of the factor presented alone.This shows us that when variables were presented with other variables, they were not seen to be as important nor did they carry a lot of weight.