Self-Care
Dating coach James Bauer writes:
Psychologists call it “self-care.” I have seven super-easy suggestions[3] for injecting some self-care into your daily routine.
1. Know your no’s.
Make a literal list of things you don’t like to do. It might include optional activities you don’t enjoy (like seeing horror movies), or limiting when you do some things (like not checking work email at night). Then don’t do those things.
2. Don’t skimp on sleep.
Sleep can help keep your appetite in check, boost cognitive ability, lift your mood, help your body heal, and even lower your blood pressure.[4]
3. Workouts help you work stuff out.
Besides burning calories and toning muscle, exercise improves your mood, super-charges your energy levels, and helps you get better sleep.[5]
4. Give meditation a try.
Mediation keeps your brain young, works as a natural antidepressant, helps you concentrate, and reduces anxiety.[6]
5. Do the family thing.
Time with family can be very rewarding. Who doesn’t like to feel loved? And if you’re not on the best of terms with your biological family, consider adjusting your definition of “family” to include what I call “chosen family” – your friends that are like family.
6. Be completely chill at least once a day.
Every day, spend at least a few minutes doing something completely relaxing. Take a bath, go for a short walk, or just veg on the couch without trying to accomplish anything.
7. Be completely selfish at least once a day.
Every day, do something purely for your own pleasure. Hang out with a friend, read fiction for fun, or treat yourself to a really good meal.
Ethics, Happiness and Choice—Miles's Economics 4060
Course Evaluations: https://colorado.campuslabs.com/courseeval
Term Paper
Due by 11 PM Friday, May 5, 2023.
Your term paper should incorporate a revised version of what you wrote for your Analysis Task and be 4-6 pages longer than that revision of what you wrote for your Analysis Task. That is, the rest of your paper beyond what you wrote for the Analysis Task should be 4-6 pages.
Here are some of the main things I’ll look for in your term paper:
Write well: have a thesis statement and a theme that you follow through
Critique a paper in the academic literature on topics related to things we have discussed in class. Don’t choose one of my papers. But the references lists in my papers on happiness and of the other papers listed below are good places to find a paper.
(Drilling down on point 2.) Assume that the paper has flawed statistical interpretation. (In particular, results are typically overinterpreted to make them sound more exciting.) Do better on the statistical interpretation front. Use all of the things you learned about statistical interpretation in class that are relevant. (I’ll subtract points if a statistical interpretation principle was clearly relevant to what you are critiquing or to your own analysis and you don’t discuss it and add points if you do a great job discussing an issue.) The goal is not to solve everything but to show your awareness of the issues and do what can readily be done to think about what the issue implies. (For example, it is great if you can say which direction a bias is.)
Weave in a revision of your analysis task. The integration of this with your critique of a paper in the literature doesn’t have to be perfect, but it is a plus if you can make the critique of a paper in the literature and your own statistical analysis fit together with a theme.
Be timely: because of the exigencies of making grades for this and my other course, it is especially important that I get these term papers by the due date. However, I will still accept them, but with some points off, up to two days late.
The main idea for the term paper is to discuss an academic journal article on well-being skeptically. (I’ll add suggestions for academic journal articles to write about to this post when I get a chance.)
Here are some ways you might want to be skeptical:
Scale-use differences might be creating an illusion. (How?)
There is likely to be statistical bias relative to what the author or authors seem to be claiming or implying. (Make sure to explain which direction you think any story of possible statistical bias would bias things. Is the estimate in the paper likely to be higher than the truth or lower than the truth? What does that say about the truth?)
A result that has a nominal p-value of 5% (t-statistic of 2 or so) really has about a 50% chance of being spurious, as indicated by replication studies. (By contrast, a result with a nominal p-value of 1/2 % (t-statistic of 3 or more) has only about a 5% chance of being spurious, and so is relatively trustworthy. Here though, “trustworthy” had to be taken in a narrow sense. Something is probably going on with that coefficient, but what is going on may be very different from what the authors claim. (See for example the rest of this list of reasons to be skeptical!)
Happiness is not the same as utility. As my coauthored papers “Utility and Happiness,” “What Do You Think Would Make You Happier? What Do You Think You Would Choose?” “Can Marginal Rates of Substitution Be Inferred from Happiness Data? Evidence from Residency Choices” and “Beyond Happiness and Satisfaction: Toward Well-Being Indices Based on Stated Preference” suggest, there are many distinct meaning of happiness. Among them are:
utility
feeling happy
what people report on a survey about their happiness, life satisfaction or position on the ladder of life (all of which have a lot of data available)
Aristotelian noble happiness, often called “eudaimonia” or “eudaemonic well-being” in the literature
Other theoretical issues
Papers with References Lists in which You Can Find a Paper to Critique (Note: Don’t Critique a Paper with Miles as a Coauthor—those papers are only included because they have highly relevant references lists):
“Challenges in Constructing a Survey-Based Well-Being Index,” by Dan Benjamin, Kristen Cooper, Ori Heffetz and Miles Kimball
“A Well-Being Snapshot in a Changing World,” by Dan Benjamin, Kristen Cooper, Ori Heffetz and Miles Kimball
“What Do You Think Would Make You Happier? What Do You Think You Would Choose?” by Dan Benjamin, Ori Heffetz, Miles Kimball and Alex Rees-Jones
“Can Marginal Rates of Substitution Be Inferred from Happiness Data? Evidence from Residency Choices,” by Dan Benjamin, Ori Heffetz, Miles Kimball and Alex Rees-Jones
“Beyond Happiness and Satisfaction: Toward Well-Being Indices Based on Stated Preference,” by Dan Benjamin, Ori Heffetz, Miles Kimball and Nichole Szembrot
“Happiness Dynamics, Reference Dependence and Motivated Beliefs in US Presidential Elections,” by Miles Kimball, Collin Raymond, Jiannan Zhou, Junya Zhou, Fumio Ohtake and Yoshiro Tsutsui
“What Do Happiness Data Mean: Theory and Survey Evidence,” by Dan Benjamin, Jakina Debnam Guzman, Marc Fleurbaey, Ori Heffetz and Miles Kimball
“Adjusting for Scale-Use Heterogeneity in Self-Reported Well-Being,” by Dan Benjamin, Kristen Cooper, Ori Heffetz, Miles Kimball, Jiannan Zhou.
“Utility and Happiness,” by Miles Kimball and Bob Willis
“The Paradox of Declining Female Happiness” by Betsey Stevenson and Justin Wolfers
“The Female Happiness Paradox” by David Blanchflower and Alex Bryson
Quiz #2 (Wednesday, April 26, 2023)
Blog Posts and Articles to Read to Prepare for the Quiz
“Desiderata,” by Max Ehrmann (Optional: Wikipedia article on “Desiderata”)
“Measuring the Essence of the Good Life,” by Dan Benjamin, Kristen Cooper, Ori Heffetz and Miles Kimball
“Challenges in Constructing a Survey-Based Well-Being Index,” by Dan Benjamin, Kristen Cooper, Ori Heffetz and Miles Kimball
“Let's Set Half a Percent as the Standard for Statistical Significance,” by Miles Kimball
“What Do You Think Would Make You Happier? What Do You Think You Would Choose?” by Dan Benjamin, Ori Heffetz, Miles Kimball and Alex Rees-Jones
“Judging the Nations: Wealth and Happiness Are Not Enough,” by Miles Kimball
“There's One Key Difference Between Kids Who Excel at Math and Those Who Don't,” by Miles Kimball and Noah Smith
Statistical Interpretation Principles to be Tested on the Quiz
Whether the direction of bias is 0, +, - or ? given an arrow diagram with +’s and -’s on the arrows
Multicollinearity in practice
Note: Using the false discovery rate (FDR) approach to deal with multiple hypothesis testing will not be tested on this quiz, but you will be expected to use it where appropriate in your term paper. Note that “Who Leaves Mormonism?” and “A Well-Being Snapshot in a Changing World” use the false discovery rate approach.
The Analysis Task
The Analysis Task is now posted on Canvas.
Understanding the data:
This link takes you to the public Dropbox folder with the data files. Start by looking at the README file. Our Well-Being Measurement Initiative Research Assistant Jeffrey Ohl can answer your questions: johl@umich.edu Make sure to include Jeffrey's email address on any question about the data. He'll do most of the answering about the data himself. You can do almost anything for the analysis task; it just needs to be interesting.
This is a link to take the Baseline survey so you can understand what data is available and what questions the data are based on: https://wiagl.gitlab.io/survey-baseline/?workerId=[enter your name, or your number plus same random numbers]
This is a link to the Life & Psyche survey so you can understand what data is available and what questions the data are based on: https://ucla.qualtrics.com/jfe/form/SV_8kK2HMh6YrGSEF8. This is the survey that has most of the psychological indexes. (Baseline only has a few.) It has some other miscellaneous questions, too. Only some of the people who did Baseline went on to do this survey.
This is a link to take the Bottomless HIT survey so you can understand what data is available and what questions the data are based on: https://wiagl.gitlab.io/survey-bottomlessa/. Only some of the people who did Baseline went on to do this survey (an overlapping, but different subset than those who went on to do the Life & Psyche survey.) You don't have to do all of this—just keep going until you have an idea for what analysis you want to do. The very first Block is a repeat of what is on Baseline, but it gets different after that.
Relevant Powerpoint File:
The analysis task is due by 11 PM Saturday, March 18. It needs to report the analysis with tables or figures and also have text that clearly explains the analysis. The idea is that this is like one section of a paper.
If you have an idea of what to do for the analysis task, just send me and Jeffrey an email and I'll give a reaction of how interesting I think it is, and maybe a suggestion for a tweak.
Seeing the analysis and its explanation as one section of your term paper. (Your term paper is due later, at 11 PM on Wednesday, May 3—the evening after the last class.) The idea is to make this analysis part of a larger discussion.
Including figures and tables, the analysis task should be at least 5 pages. I'll take a risk and not put an upper limit on the length of the analysis task. (The term paper beyond the analysis task should be between 5 and 10 pages, with closer to 5 being preferred.)
How to structure your writeup of the Analysis Task:
You can design a different structure, but a typical writeup could look like this:
Here is an interesting question or questions. The answers matter (people care or should care) because: …
Here is a statistical analysis that seems to have some bearing on this question or questions:
On the surface the statistical results seem to say: …
However, the following confounding factors could be giving rise to an illusion, making it seem like something is there that isn’t or that something is bigger or different than it really is.
Don’t forget to talk about the confounding factors! (4.)
Here is a Q&A about the analysis task:
Q:
What is the level of analysis you are expecting for this assignment? I’ve taken some stats classes, so I’m familiar with hypothesis testing and regression, but since this class doesn’t have a stats prerequisite I’m not sure how in depth I should go for this assignment.
Since most aspects of wellbeing are correlated with each other, it seems to difficult to use regression to analyze relationships between these aspects without running into reverse causality, cousin causality, or both. My knowledge of stats isn’t sufficient to avoid these problems in cases where instrumental variable regression isn’t a viable alternative. I’m wondering what you would suggest that I do to avoid this issue.
A:
At the low end, it could be simply some scatter plots or bar charts or other interesting graphs.
I don't expect you to have consistent estimates of anything, rather to be able to discuss any biases there might be in the estimates you do get, relative to something interesting. Please make the attempt to figure out the sign (+ or -) of any bias you discuss, and say what that would mean for the truth of the interesting thing one might care about. If there are multiple biases, try to figure out the sign of each one, even if all the biases put together can't be signed because some biases are likely to be + and others are likely to be -. Also, discuss whether you think a bias is likely to be large or small.
Advice for the Analysis Task:
Use lots of graphs. I love scatterplots, but other types of graphs and figures can be good, too.
It’s fine to do some statistics on individual variables, but make sure you do something that relates pairs of variables to each other.
Do some formal statistical tests.
When you test more than one hypothesis, set it up so you can do the multiple hypothesis test correction using the False Discovery Rate procedure!
Make a distinction between being significant at the 5% level and being significant at the 1/2 % level.
If something isn’t statistically significant, you say “I can’t reject the null hypothesis that …” NOT “I reject the alternative hypothesis.” If you want to reject a hypothesis, you have to set it up as a null hypothesis.
Recognize reverse causality and cousin causality, including the consumer-theory-esque model I gave in class of how resources broadly construed help all good things, leading to the general principle (with only a few exceptions) that “All good things are positively correlated.” (This is a statement about the cross section.
Define variables in full. You need to act like your reader doesn’t know what the abbreviations mean. So write out the full text of the aspects, and describe fully all other variables. (You will see that we do this in our papers.)
Don’t order response categories alphabetically! They need to be ordered logically. For example, political leanings should be ordered from Left to Right and levels of education should be ordered from less to more.
When you have interesting results for several variables that are along the same lines, think of creating a simple index to get more statistical power. That is, take simple averages of similar variables and treat that simple average as an index.
Think about how nearly statistically exogenous your right-hand-side variables are. Other things equal, regressions with more nearly statistically exogenous right-hand-side variables are more interesting. That doesn’t mean you can’t do other things. Just think about this dimension.
Think seriously about scale use. Any statistical analysis you do with aspect-of-well-being data you can probably do both with the raw aspect ratings and with (aspect rating - average of calibration questions). Doing both of those analyses will be much more interesting than just the one analysis.
2023 Quiz #1 (Wednesday, March 15)
2023
Histogram for Quiz #1 (Remember that a lot of your grade is writing assignments. The quizzes are a modest percentage of your grade.)
Blog Posts and Articles to Read to Prepare for the Quiz
Cognitive Behavioral Therapy for Insomnia Can Prevent Major Depression
An Example of Ideology Leading to Bad Statistics and Social Injustice
Why a Low-Insulin-Index Diet Isn't Exactly a 'Lowcarb' Diet (Focus on the interpretation of the DIETFITS study.)
Exorcising the Devil in the Milk (an example of trying to interpret less-than-perfect evidence, and of scatterplots)
Also, to help with the style of question I often use, take a look at “Critical Reading: Apprentice Level”
Rules for Parsing Unsigned Arrow Diagrams
For OLS to be unbiased, you need Cov(X,epsilon) = 0 (exclusion restriction for OLS)
For IV to be unbiased, you need
Cov(Z,epsilon) = 0 (exclusion restriction for IV)
AND
Cov(Z,X) is not zero (relevance of the instrument)
How do you tell if a covariance is zero or not zero?
It works the same way for all 3 cases. Let me call the two things you want to know if the covariance between is zero or not A and B. (A and B come from the set {X,epsilon,Z).
The covariance is NOT zero if EITHER
There is a path following the one-way signs from A to B
There is a path following the one-way signs from B to A
There is a path following the one-way signs from something else to A, and a path following the one-way signs from that same thing to B.
To show a covariance is zero, you have to check a lot of things. You need:
There is no path following the one-way signs from A to B
There is no path following the one-way signs from B to A
There is no other thing from which there is a path following the one-way signs to A, and from which there is a path following the one-way signs from that same thing to B.
Paper on "12 Rules for Life: An Antidote to Chaos"
This assignment is now posted on Canvas. If you are way ahead of the game, you could even submit it now!
3-5 pages
Due Tuesday, February 28, 2023 at 11 PM
Choose one of the 12 rules that you think, if more fully implemented, would make a big positive difference in either your life or in American society (or in your home country’s society). Lay out how it could help you get more of what you want (or reduce your suffering) or help people in our society more generally get what they want (or reduce the suffering of people in our society). Go into depth. Also, answer: “Are there ways you would modify the rule to make it even better?”
If you disagree with all 12 rules, choose one and write about how it is bad.
Paper on "Happiness: A New Science," by Richard Layard
3-5 pages
Please put your name inside the document! I print them out to read them, and it is then hard to match up the pieces of paper with names. I will subtract points if you don’t put your name in there.
Documents can be Word documents or pdf.
Make sure to provide evidence in your paper that you have read the whole book. (For example, you might choose appropriate quotations from the book sprinkled from beginning to end, or simply address issues in the book that are raised near the end as well as issues that are raised near the beginning.
Due 11 PM Monday, February 13, 2023.
Themes/Questions to Answer:
What is the “conventional wisdom” about happiness that Layard presents?
Where is that conventional wisdom right and where is it wrong?
Where the conventional wisdom is right, what are the implications for your own life?
Note: Make sure that you don’t sound too much like ChatGPT. A hint about that is to be personal about “what are the implications for your own life.”
Ideas for Using ChatGPT
The key rule is that you need to treat text (words) from ChatGPT the same as any other text (words) that you find online. You need to clearly label those words as due to ChatGPT. And, of course, you shouldn’t have a ridiculously high percentage of words in your paper be someone or something else’s words. The same rule applies to any other AI. The way to cite text from ChatGPT is to clearly set out the prompt you gave ChatGPT before the text you got from Chat GPT.
That said, as long as you clearly label text from ChatGPT as coming from there, there are many interesting ways to use it in a paper. Here is an article with some good ideas.
In addition to being an automatic F if you are determined to have used ChatGPT without clearly labeling the words from ChatGPT as coming from ChatGPT, one of your tasks in each paper is to make your own words sound different from ChatGPT. You will get a lower score if you sound too much like ChatGPT.
Book Choices for the Group Oral Presentation; First Assignment
Timing: The presentations will be during the 6 classes from Monday, January 30, through Friday, February 10.
Six Books for Six Teams:
Factfulness: Ten Reasons We’re Wrong about the World—and Why Things Are Better Than You Think by Hans Rosling
GDP: A Brief but Affectionate History by Diane Coyle
The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom by Jonathan Haidt
Beyond Order: 12 More Rules for Life by Jordan Peterson
The Coddling of the American Mind by Jonathan Haidt
The Tyranny of Merit by Michael Sandel
First Assignment, Due 11 PM Friday, January 19 (I pushed this back a day because of our snow day on Wednesday; I’ll give you a preview of each of the books on Friday. But I need it at 11 PM Friday so I can assign you to book groups over the weekend and you can get started reading.)
1. Write a few sentences about what drew you to this class and what you hope to learn and get out of it.
2. List in order your 1st, 2d and 3d choices for the additional book you want to read and do a group presentation on.
3. Write a few sentences on why you are particularly interested in reading your 1st choice. (Optional: you can write about your 2d and 3d choices, too if you want to.)
Note:
I tried to post this assignment on Canvas. You can submit your answers there. (If you can’t figure out how to submit it on Canvas, you can send it to me in the body of an email.)
Not this time:
The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson (Related blog posts laid out here.)