21 Empirical Findings About Human Happiness

In the American Declaration of Independence it states that all men are endowed with certain unalienable rights, one of which is the pursuit of happiness. Yet up until very recently, we humans did not have much information about what actually makes us happy. In the US, most people would agree that you shouldn't cheat on your spouse, that you should live within your means, and that it's a good thing to show charity to those less fortunate.

The problem is we don't actually know -- in a scientific sense -- whether these behaviors actually make us happier at the end of the day. It wasn't until the late 1950s  that any serious scientific rigor was applied to measuring happiness, and even then it was a nascent branch of psychology without much public interest.

Researching Happiness

I came to be interested in happiness research as the result of watching "Why are we happy?" For those of you interested in the answer to that question, I highly recommend you take the 20 minutes and view the talk. It got me pumped up enough to read a slew of research papers that have informed my outlook on everyday life and made me a happier person.

In the course of my readings, I decided it would be a good idea to write up my findings in bite-sized chunks so that others can better understand themselves and research the topic more effectively. Think of this blog post as a cliff notes to happiness research. I don't go into detail on any of the points, and if you want further information, I'd recommend reading the resources at the bottom of the page.

Counter-Intuitive Findings

1. It is often easier to overcome very bad events than trivially bad events because of what is called the region-ß paradox. This occurs because events that are more difficult to overcome trigger critical threshold attenuating mechanisms that allow humans to recover much faster than had the bad event been of a lesser degree. This means that in some circumstances, it is actually advantageous to make situations more difficult than necessary because it's the small stuff that gets you.
2. People systematically overestimate the positive and negative effects of future events, this is called impact bias. Example: people consistently rank becoming crippled as negatively affecting lifetime happiness in a large way. People also predict winning the lottery will have a significant positive effect. In fact, after several years,  people that experience either event are about equally happy. When asked to focus on a certain event, we forget that being crippled or winning the lottery does not continue to affect our lives in big ways for long periods of time. The change is sudden, big, and then we adapt.
3. We experience greater happiness from unexplainable positive events than from explainable positive events. Barring any creepy overtones, if someone gives you $5 and tells you it's for a known cause, you will be less happy than if the same person gave you $5 and walked away without explaining why they gave you the money.
4. We think that losing will hurt more than winning will please us, but in reality, losing doesn't hurt as much as we expect. This finding suggests that we should be more adventuresome. The old saying "nothing ventured, nothing gained" isn't a truism for nothing.
5. Try not to make choices based on comparison, but based on values. Ex: don't choose a job because of it's comparative "betterness" to other offers you have received, because the comparison no longer exists when you make your choice and begin working. Instead, choose a job because it fits your values (eg, "close to home," "reasonable hours," etc).
6. We remember state changes most vividly, and the ending state most vividly. For example, even if a marriage is good for 15 years and bad for 2, the ending state has a disproportionate effect on the happiness derived in the future.
7. Typical life events stop effecting your happiness after 3 to 6 months, although in the intervening time period, they can raise your happiness. The implication is that big life-events don't effect us that much (if at all) in the long term.
8. Widowhood and divorce are listed as the two most stressful events in adulthood, marriage was listed as #7. In addition, long term studies of marital satisfaction find that during the first  years of marriage, life satisfaction declines. Later in marriage, however, satisfaction rises. This is small wonder. During the first years of marriage people merge their entire life with someone else. It stands to reason that after a while, the stress of adapting turns to satisfaction with sharing life's journey.
9. Happy people get married more often than sad people. People who get married tend to rank themselves .25 points (on a 0-10 scale) higher on average before marriage than those who do not get married.
10. The heritability of well being is thought to be between 50-80%. Stated another way, 50-80% of your day to day happiness level is thought to be genetically determined. This finding is referred to in the research literature as the "set point theory" (not to be confused with the identically named theory about weight loss). Where we are born, cultural aspects of life, demographics, age, gender,  and ethnicity all account for a mere 8-15% of our total minute-to-minute  happiness. So if you're an optimist, 42% of your happiness is in your hands. If you are a pessimist, you only have 5% to play with.
11. Some of the big attitudinal factors that can positively affect happiness are: general optimism about life circumstances, an inclination to avoid social comparisons, and the tendency to feel a sense of optimism about one's life.
12. Personal projects are worth pursuing. Accomplishing and  continuing to accomplish projects and pursuits of personal importance have lasting effects on day to day happiness. Example: research has found that almost nobody regrets spending time developing skills or hobbies, even when the pursuit is discarded later and never resumed.
13. People regret actions more in the present. People regret innaction more in the long term. Example: you are much more likely to regret going on a disastrous trip to Europe the week after returning, but in the long term you are much more likely to have regretted not going at all. One possible explanation for why this happens is that we over-emphasize the effects of ambiguous happiness  (what could have been if we had gone to Europe) and minimize concrete badness (that train wreck we caused in Germany).
14. We get happier with age, up until around 65, after which happiness tends to stabilize. Only in the very old (some studies suggest 95+) does day to day happiness decline.
15. We don't end up regretting circumstances out of our control. Persons that have experienced polio and other life altering (but unavoidable) mishaps don't tend to list those events as regrets when asked. This should make you worry less: things beyond our control generally don't affect our long term happiness.
16. Self control is a muscle. Presenting people with two tasks that require self control one right after another shows that the successful performance on the second task diminishes significantly. In other words, self control is a muscle that we can exhaust. This explains why alcoholics and dieters more often lapse when they are experiencing bad moods, frustration, and stress. Although theoretically possible, there isn't much empirical evidence that we can increase our self control with practice.
17. It pays to be an extrovert. Extroversion as a personality trait correlates strongly with experiencing more positive interactions as well as achieving higher happiness stability. So for us introverts (myself included), developing your ability to be an extrovert appears to pay off.
18. We sometimes overwhelmingly prefer to be less happy. Even though we may prefer eating a lollipop to eating spinach or broccoli, we tend to choose a mixture if presented the choice. This is strange because we enjoy repeated preferred experiences (the lollipop) more in the moment but less in retrospect. We prefer to sacrifice total happiness for higher retrospective happiness, even though studies show retrospective pleasure is by far less important to lifetime happiness than moment-to-moment enjoyment.
19. Income expectations do not appear to effect happiness, ie, a person who grew up poor with expectations of continued poverty is not significantly more happy if they earn a lot of money. In addition, there is substantial evidence that income that exceeds $60,000/year doesn't make you any happier.
20. People don't always prefer less pain. People care less about total pain (ie, how long a pain is experienced) and more about peak pain level, ending condition, and the time trend of the pain. Example: controlling for peak pain level and the result of the operation, people show no retrospective preference for a 4 minute colonoscopy or a 69 minute colonoscopy!
21. The desire to get discomfort out of the way early and quickly appears to be a universal preference. People also tend to prefer sequences of events that end better than they start, even if the total sequence results in less overall happiness. If you offer someone a salary that increases from $35k-50k over a year as opposed to a salary that goes from $70k-50k, people will widely prefer the first, even though it earns them $35k less. The preference for best thing last changes according to a documented "Magnet Effect" which states that if events/decisions occur close enough in time we treat them like a sequence.

The above studies are just a sample of what I've been reading, and I expect to continue my research slowly in my free time. I will probably post here again when I have another batch of interesting nuggets. What I have learned is that there is growing momentum in the quest to increase human happiness and I am looking forward to learning ever more about how to make my life more enjoyable.

Bibliography

Gilovich, Thomas, and Victoria Husted Medvec. (1995). "The Experience of Regret: What, When, and why," Psychological Review 102(2), 379-395.

Kahneman, D., D.L. Frederickson, C.A. Schreiber, and D.A. Redelemeier. (1993). "When More Pain is Preferred to Less: Adding a Better End," Pyschological Science 4, 401-405.

Loewenstein, George, and Drazen prelec. (1993). "Preferences for Sequences of Outcomes," Pyschological Review 100(1), 91-108.

Brunstein, J. (1993). Personal goals and subjective well-being: A longitudinal study. Journal of Personality and Social Psychology, 65, 1061-1070.

Charles, S. T., Reynolds, C.A., & Gatz, M. (2001). Age-related differences and change in positive and negative affect over 23 years. Journal of Personality and Social Psychology, 80, 136-151.

Diener, E., Sandvik, E., Seidlitz, L., & Diener, M. (1993). The relationship between income and subjective well being: Relative or Absolute? Social Indicators Research, 28, 195-223.

Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin, 126, 247-259

Ratner, R.K., Kahn, B.E., & Kaneman D. (1999). Choosing less-preferred experiences for the sake of variety. Journal of Consumer Research, 26, 1-15.

Hill, C.T., & Peplau, L.A. (1998) Premarital predictors of relationship outcomes: A 15-year follow-up of the Boston Couples Study. IN T. Bradbury (Ed.), The Developmental course of marital dysfunction (pp. 237-278). New York: Cambridge University Press.

Brunstein, C. Joachim (1993) Personal Goals and Subjective Well-Being: A Longitudinal Study, Journal of Personality and Social Psychology Vol 65, No. 5, 1061-1070

The Peculiar Longevity of Things Not So Bad, Daniel T. Gilbert, Matthew D. Lieberman, Carey K. Morewedge, and Timothy D. Wilson, 2004, American Psychological Society

Affective Forecasting: Knowing What to Want, Timothy D. Wilson and Daniel T. Gilbert, 2005 American Psychological Society

Thinking about Values in Prospect and Retrospect: Maximizing Experienced Utility, Joel Huber, John Lynch, Kim Corfman, Jack Feldman, Morris B. Holbrook, Donald Lehmann, Bertrand Mueir, David Schkade, Itamar Simonson

Events and Subjective Well-Being: Only Recent Events Matter, Eunkook Suh and Ed Diener, Frank Fujita, Journal of Personality and Social Psychology, 1996

Reexamining Adaptation and the SEt Point Model of Happiness: Reactions to Changes in Marital Status, Richard E. lucas, Yannis Georgellis, Andrew E. Clark, Ed Diener, Journal of Personality and Social Psychology, 2003

Pursuing Happiness: the Architecture of Sustainable Change, Sonja Lyubomirksy, Kennon M. Sheldon, David Schkade, Review of General Psychology, 2005, Vol. 9 No. 2 111-131

IMDB Score Bias: It's Temporal

This post was originally published on 2/7/2011.

I'm a nerd, economist, and a movie snob. Sometimes it makes me hard to deal with.

My brother has a natural fear of picking movies with me. During our holiday visits home we invariably try to watch a movie and it ends in eyes being rolled in my direction. My family has taken to calling any movie I pick as a "depressing indie drama."

I don't think of this as being difficult, I think of it as getting the most out of my time. Having seen thousands of great movies, I have trouble committing two hours to a movie of dubious quality. In an effort to avoid wasting time on bad and mediocre flicks, I am on a quest to better predict how much I will enjoy a given movie. I've rated more than 700 movies on Netflix, I visit IMDB about 25 times week, I've tried Flixster, Rotten Tomatoes, and the blogs of well-known critics. The goal is to accurately correlate my movie-watching happiness with the ratings provided by these sources. So far the results are disappointing. No one source accurately predicts my preferences. Even inter-comparing and creating composite indexes frequently leads to contradictory predictions. To date, the best predictor I've found is a film's IMDB rating, but this number is far from perfect.

IMDB ratings are worst when movies are newly released. For a film like Citizen Kane, the IMDB score is accurate, and no wonder: enough people have seen it to decide how good it is. In fact, Orson Welles' masterpiece has 145,319 ratings on IMDB, a score of 8.6/10, and is listed by the American Film Institute as the best movie ever made. [1] Citizen Kane is pretty similar to other critically acclaimed films on IMDB. Among the top ten films, the average number of IMDB votes is 152,073 and the median score is 8.45. With so many ratings, my guess is that these movies are more accurately rated than a movie with 1% as many reviews that was released last week.

Take Inception for example. When it was released it had a rating of 9.3 on IMDB and thousands of reviews. But how could this be? Was Inception actually a better movie than Citizen Kane, Casablanca, The Godfather, Gone with the Wind, Lawrence of Arabia, The Wizard of Oz, The Graduate, On the Waterfront, Schindler's List, and Singin' in the Rain?  Having seen all of these films, I had a hard time believing it.

So, I hypothesized that IMDB ratings were biased upwards for young movies. When new movies come out, the first people to see them are early adopters and critics. As an example, someone disinterested in a new film may see it eventually [2], but they are unlikely to see it the first day it comes to their local theater. My contention was that seeking out such pre-releases, in combination with marketing and release hype, would select and reinforce overly-positive movie reviews.

To test this theory, I spent three months sampling a randomly-selected group of 21 new releases. I started sampling on November 9th by finding IMDB's list of upcoming movies and recording the first data point for all of them [3]. I then checked the ratings once weekly to see if my prediction about prerelease hype held up to a little empirical rigor. My sample was surprisingly diverse. Among the movies I sampled there were big budget Hollywood films like Tron: Legacy as well as indie films like Rare Exports [4]. Because some of the films were slated to release later in the month of December and some had pre-screeners who rated the movies before a popular release, I didn't have an equal number of data points for each film. Almost every film did reach score equilibrium; the score remained stable for at least three sampling periods (three weeks). Here is a time series for the films. I've omitted the titles since it would clutter the graph too much:

Looking at the graph is a bit confusing, and there isn't a clear trend. So I turned to the numbers. With a little statistical crunching I found that the average movement in rating was -.2125, significant at 95% confidence. In other words, new movies do have inflated IMDB ratings, on average those ratings are .2 points above where they will eventually settle.

The greatest volatility in rating was in the first two sample periods, which is to be expected. The Tempest and Casino Jack were the biggest losers (shedding 1.6 points in the 3 month period). There were several films that appear to have been correctly assessed from the get-go and had no rating change after 12 weeks: I Love You Philip Morris, The Tourist, The Fighter, Little Fockers, and a French film by the name of The Illusionist. The rest suffered small declines in score that are consistent with my theory. 

The takeaway here is that if you are asked to watch a new release, assume that the IMDB rating is overly-optimistic by about a fifth of a point, then go anyway and have a good time.

[1] Even a film snob like me must admit that it is ridiculous to make such a claim but it sure sounds definitive.

[2] I suspect the biggest reason that disinterested people see films is social pressure.

[3] The equivalent page for this week would be here.

[4] I tracked all of the following films: Black Swan, I Love Your Phillip Morris, Rare Exports, The Warrior's Way, The Tourist, The Tempest, The Chronicles of Narnia: Voyage of the Dawn Treader, The Company Men, The Fighter, Tron: Legacy, Yogi Bear, How do you Know, All Good Things, Rabbit Hole, Casino Jack, Little Fockers, True Grit, Somewhere, The Illusionist, Gulliver's Travels, and Country Strong.

Your Child's Name Won't Limit Their Digital Future

What are you planning to name your children? If you answered with any common first and last name combination, your child may be at a digital disadvantage. They will be condemned to a life of appending numeric sequences to their user names, picking off-brand Twitter handles, and choosing unrelated domains for their websites. Their Facebook and LinkedIn profiles will be difficult for new acquaintances to locate, Google results will misrepresent them to future employers, and their children will have an even harder time of it.

At least, that's what I thought several weeks ago.

It's easy to imagine that any given name you choose for a child will be common enough that somebody out there has grabbed the domain, the Twitter handle, the Facebook username, the Gmail address, and countless other digital identifiers. In this paranoid world-view, your child is relegated to being a second class citizen of the net (or whatever it becomes) simply by dint of having a common first/surname combination.

But how large of a problem is it really? To answer the question, I did some research on full name variation and came away surprised.

It is trivial to find popular baby names for a given period of time, but finding the frequency of full names in the US is another matter. The CDC and census bureau both don't have full name information for confidentiality reasons, and the only place I was able to find a list of unique first and last names was from a company doing greyhat Facebook advertising and data mining [1]. Here is their list of the 100 most popular first and last names on Facebook in 2009 [2].

I then ran these names through a bulk domain availability search (where the name "John Smith" turns into "johnsmith.com"). Not a single .com domain is currently unregistered.

Of course, that test doesn't tell me much except that at the very edges of the full name distribution, most of the domains are taken. The bigger question is how long the tail is. If 98% of first name/last name combinations are not contained within the top 100, then your kid will be fine unless you do name them John Smith.

Since I wasn't able to get my hands on a dataset for full names, I decided to approximate the frequency of firstname + lastname duplicates based on individual surname duplicate frequency in the US. Because surnames have fewer letters than first names and surnames combined, there are fewer possible combinations. This means that there should be a lot more duplicate surnames than duplicate first name/surname combinations. So, if it turned out there was a lot of surname repetition, I wouldn't be able to conclude much about how common first name/surname combinations are, I would just know that they are less common than the data I can see. However, if duplicate surnames turn out to be relatively uncommon, I could conclude that first name/surname combinations are more uncommon still, and disprove my suspicion that the uniqueness of my child's name is important to their digital future.

Among surnames, the top 100 most popular constituted 16.4% of the US population, the top 1000 accounted for 38.9%. That means that the majority of surnames lie in the long tail rather than the head of the distribution. We can therefore assume that the distribution of full names is even more skewed towards the long tail since the domain space is substantially larger. This assertion is supported by the Facebook data above.

This Wikipedia graph suggests there were around 350,000,000 users on Facebook in 2009. The total number of names in the top 100 comes to around 592,000, which represents just .1% of users on the site.

The conclusion I have to draw is that unless you name your son John Smith and your daughter Sarah Smith, they will have perfectly viable digital identities available to them when they graduate from college and need to start looking presentable to the rest of the world. So rest easy; I know that I, for one, want to name my first child Ellis. According to White Pages Names, it seems it is suitably obscure, and hey, I notice the domain "ellissaines.com" isn't registered. I think I'll register that ... just in case.

Special thanks goes out to the staff of the Alden Reference Library at Ohio University for helping me obtain this link and other vital statistics I used in this blog post! Specifically Sherri Saines (@bibliosanity), Tim Smith, Kelly Broughton, and Cary Singer.

[1] The company was later banned from scraping Facebook, which is presumably why their data is so old.