25 interesting facts #4

76. Wealthier Americans have per capita footprints ∼25% higher than those of lower-income residents, primarily due to larger homes (Goldstein et al. 2020)

77. Having a baby the week before an election reduces turnout by 26 percentage points for mothers and 13 percentage points for fathers (Bhatti et al. 2019)

78. Early investments in state capacity promote persistently higher levels of social capital (Jensen and Ramey 2020)

79. Variables on scatterplots look more highly correlated when the scales are increased (Cleveland et al. 1982)

80. Official statistics systematically exaggerate development progress in several African countries (Sandefur and Glassman 2013)

81. People overestimate the width of their face (Longo and Holmes 2020)

82. Higher temperatures reduce economic growth in poor countries (Dell et al. 2012)

83. Democratic and Republican physicians provide different care on politicized health issues (Hersh and Goldenberg 2016)

84. In maritime disasters, captains and crew survive at a significantly higher rate than passengers (Elinder and Erixson 2012)

85. Joining a group diverts regret and responsibility away from the individual (El Zein and Bahrami 2020)

86. Later bedtimes predict President Trump’s performance (Almond and Du 2020)

87. People find persons with similar political affiliations slightly less attractive, less dateable, and less worthy of matchmaking efforts (Easton and Holbein 2020).

88. Family members and neighbors of 9/11 victims became significantly more Republican and active in politics (Hersh 2013)

89. A substantial fraction of individuals enjoy the intrinsic value of power (Pikulina and Tergiman 2020)

90. Police officers speak significantly less respectfully to black than to white community members in everyday traffic stops (Voigt et al. 2017)

91. Because aircraft observations are important for monitoring the weather, weather forecasts were worse during the COVID-19 pandemic as airline traffic decreased (Chen 2020)

92. Dictators use entertainment restrictions to appeal to their backing coalition (Esberg 2020)

93. Cycle commuting is associated with a lower risk of CVD, cancer, and all cause mortality (Celis-Morales et al. 2017)

94. Gendered notions of brilliance are acquired early and have an immediate effect on children’s interests (Bian et al. 2017)

95. Persons who have stronger physiological reactions to negative stimuli do not hold more conservative attitudes (Fournier et al. 2020)

96. The more people believed that Osama Bin Laden was already dead when U.S. special forces raided his compound in Pakistan, the more they also believed he is still alive (Wood et al. 2012)

97. Radical right party success causally affects mainstream parties’ positions (Abou-Chadi and Krause 2020)

98. An aspect of a Japanese folk superstition might increase induced abortion rates every sixty years (Kaku 1974)

99. Unemployment news worsens incumbents’ electoral prospects (Garz and Martin 2020)

100. Cynical individuals generally do worse on cognitive ability and academic competency tasks (Stavrova and Ehlebracht 2019)

Hvor mange vil stemme på Veganerpartiet?

Forleden kunne medierne rapportere, at Veganerpartiet har fået underskrifter nok til at stille op til folketingsvalget. Denne nyhed blev slået stort op. Ekstra Bladet gik eksempelvis – som andre medier og vanen tro – direkte til breaking news:

Efterfølgende har det dog været begrænset, hvor meget vi har hørt til partiet. Ikke desto mindre er et af de oplagte spørgsmål, hvor stor opbakning Veganerpartiet vil have blandt vælgerne. Når et nyt parti melder sin ankomst på den politiske arena, vil de første meningsmålinger være afgørende for, om et parti kan få momentum eller ej i den videre dækning af partiet.

Der kom dog ingen meningsmåling(er) i kølvandet på, at Veganerpartiet havde fået underskrifter nok til at stille op. Dette skyldes at ingen analyseinstitutter havde Veganerpartiet som en svarmulighed i deres målinger, før Indenrigsministeriet offentliggjorde, at de er opstillingsberettigede. Dette giver god mening, men der er flere eksempler på, at analyseinstitutterne har været interesseret i nye partiers vælgeropbakning før Indenrigsministeriet har bekræftet, at partiet kan stille op.

Vi har nu fået ni meningsmålinger fra fem forskellige analyseinstitutter, der giver bud på, hvor stor opbakningen er til Veganerpartiet. De fleste meningsmålinger er fra Voxmeter, der som bekendt kommer med en ny meningsmåling hver uge i den politiske sæson.

De fleste analyseinstitutter (Voxmeter, Epinion, Gallup og Megafon) vurderer, at Veganerpartiet står til at få omkring 0,5% af stemmerne. YouGov er vanen tro vores outlier her, der giver partiet 1,5% af stemmerne og udelukker dermed ikke, at partiets reelle opbakning kan være omkring 2% (og dermed over spærregrænsen).

Kigger vi på min seneste prognose vil jeg estimere, at partiets opbakning er på omkring 0,5%. Dette er langt fra spærregrænsen og viser, at det vil blive noget af en kamp for Veganerpartiet, hvis de skal blive repræsenteret i Folketinget efter næste valg.

Der er især tre grunde til, at jeg tror, at det vil blive svært for Veganerpartiet at blive valgt ind ved næste folketingsvalg. Og ingen af dem er afgrænset til deres politik eller deres kandidater, selvom dette selvfølgelig også vil være afgørende før, om vælgerne vil stemme på partiet.

For det første er små partier på den yderste venstrefløj ikke en mangelvare. Foruden Veganerpartiet har vi blandt andet Alternativet, Lykkepartiet og Nye Grønne. Det er ganske enkelt svært at se, at Veganerpartiet vil kunne mobilisere 2% af stemmerne med så mange andre partier på venstrefløjen. Selv Alternativet ligger nu stabilt under spærregrænsen, så der er ikke mange stemmer at høste der, og vælgerne på venstrefløjen skal være bekymret for potentielt stemmespild ved op til flere partier ved næste valg.

For det andet ligger partiet lavere end hvad meningsmålingerne har vist ved andre nye partier, der er stillet op til valg i løbet af de senere år. Her kan vi kigge på Alternativet, Nye Borgerlige, Klaus Riskær Pedersen, og Stram Kurs. Konkret ser vi her på hvad de første meningsmålinger viste for disse partier:

Som det kan ses var meningsmålingerne langt mere optimistiske, omend i mindre grad for Klaus Riskær Pedersen, der som bekendt heller ikke blev valgt ind ved valget i 2019. Overordnet var udgangspunktet i meningsmålingerne ganske enkelt bedre for de andre partier end tilfældet er for Veganerpartiet.

For det tredje er der lang tid til næste folketingsvalg. Den optimistiske læsning er, at det giver Veganerpartiet tid nok til at komme over de 2%, men min læsning er mere pessimistisk. Jeg udelukker selvfølgelig ikke, at partiet kan blive valgt ind ved næste valg, men hvis de ikke allerede kan præstere bedre i meningsmålingerne i en periode, hvor der trods alt har været fokus på, at de nu kan stille op, bliver det en hård proces at komme i nærheden af de 2%. De skal som minimum satse på at BT giver dem en platform (som de har gjort for partier på højrefløjen), hvor deres YouGov-målinger kan vise, at opbakningen til dem er stor. En indvending til mine overvejelser her er, at Nye Borgerlige stillede op lang tid før folketingsvalget, men dette er i min optik en anden situation. Nye Borgerlige var opstillingsberettiget i en periode, hvor den politiske dagsorden (e.g. flygtningekrise) gav dem bedre betingelser, hvorfor de kunne forblive relevante og aktuelle på den politiske scene. Dette ser jeg ikke som værende tilfældet for Veganerpartiet.

Der er vælgere, der vil stemme på Veganerpartiet, men de første meningsmålinger viser, at der er lang vej til de 2%.

The political scientist as a blogger

Ten years ago, John Sides wrote a paper titled The Political Scientist as a Blogger. Despite the fact that the internet is not the same today as it was ten years ago, it is still an interesting read. Specifically, the paper made me think about why political scientists should (not) blog in 2020, why I don’t like most political science blogs today and why I continue to write blog posts.

It’s quite simple. You can blog for various reasons but I believe this point from the paper sums it up: “Despite the occasional frustration, blogging can be fun — in fact, this is probably the first and best reason to do it. Once blogging starts to feel like work, you are probably not long for the blogosphere.”

I agree with this statement. Especially as a consumer of political science blogs. In fact, it is the reason I don’t read The Monkey Cage anymore (the paywall is another good reason though). It used to be great (pre-Washington Post) but the average quality is low nowadays and reads more like university press releases for mediocre studies. The reason is that most contributions to the blog today are ‘blogging for impact’ (pretty much the opposite of fun). Specifically, the blog is primarily researchers using the platform to write up a few paragraphs on their new research (to increase the public outreach of their work). Write a paper, write a blog post and aim to put the ‘Monkey Cage’ hyperlink on your CV. That is the mindset. And it shows.

Noteworthy, this is not to say that there are not good political science blogs out there. For example, I really enjoy the new blog Broadstreet (a blog on historical political economy). See, for example, this post on the recent quantitative work on the Nazi extermination and concentration camps (with a focus on testing individual-level psychological theories, post-treatment bias and other relevant issues). However, it is the exception compared to what blogging looked like ten years ago.

I guess the reason is that there is no need to blog as a political scientist. Supply and demand and nobody cares. If you want to build up a profile/brand/identity as a political scientist, create a Twitter account and engage directly with the political science community. Share your work (e.g. using the #polisciresearch hashtag), retweet interesting takes, reply and discuss, follow people who share relevant observations and material. No need to blog.

Actually, blogging can be a huge waste of your time if you take the opportunity costs into account. However, there’s a lot of ways to waste time and blogging is a fun way to do it. I guess that most people blogging today are those who care the least about building a huge audience (again, Twitter is the place to be if you’re in that game). Interestingly, the more I expect a lot of people will read my blog, the less I enjoy it.

I used to share my blog posts on Twitter but I stopped doing that. The moment I share a blog post I begin to care about the metrics. The quantitative assessment of the quality. Do people like or/and retweet the post? What do people say? And that’s funny because I usually don’t care about such data (or, that’s a lie). For that reason, I found it better to ‘detach’ my blog from everything else and try to let it live in its own universe.

However, that’s not the same as I don’t have an audience. And part of that audience is made up by political scientists. There is a certain ‘pre-social media’ nostalgia related to blogging that I enjoy. I guess at least some of my readers share that longing for old school blogging. I believe most of my readers are familiar with RSS-readers and, for that reason, I don’t pay attention to whether there is a coherent theme in my blogging (i.e. a target audience). I blog about various topics (political science and non-political science related). I blog in Danish and English. I blog about personal and non-personal stuff. In sum, I don’t consider my blog a political science blog per se.

Also, another aspect I found useful in order to further ‘detach’ my blog from any metrics and considerations was to schedule each written post for some point in the (distant) future. I believe there are two advantages to this. First, when writing a blog post, I pay less attention to whether/when people will read it and what they will say when it is published. Second, I have noticed that it is great to have a few months to read a post again and maybe add a few extra considerations (or hyperlinks) if I stumble upon something interesting (for example, I added the hyperlink to the tweet by Paul Ford after having written the first draft of this post).

There are good reasons to blog as a political scientist, but these are most likely not reasons for early career researchers (or anybody else) to begin blogging. If you are a political scientist considering the blog format, I recommend you focus on your research and send your blog posts to the Monkey Cage. That’s my simple career advice when it comes to political science blogging. And that’s why I don’t read a lot of political science blogs these days.

Dark Data: Why What You Don’t Know Matters

I like data and I like books about data. Unsurprisingly, I found the book Dark Data: Why What You Don’t Know Matters interesting to read. The book is packed with fun and interesting examples of what data can and cannot tell us. For people who teach introductory statistics and are running low on examples, I can recommend consulting this book.

That being said, I have some issues with the book. While the book is about missing data, only a small part of the book deals with actual missing data (as defined by text books in statistics). This is not necessarily a problem but the book does not succeed in fully connecting the notion of dark data with the traditional understanding of missing data. The problem is that upon reading the book, most issues with data can be understood through the concept of dark data, including issues that have very little to do with data.

To understand different types of dark data, the book operates with a list of 15 different types of dark data (DD):

  1. Data We Know Are Missing
  2. Data We Don’t Know Are Missing
  3. Choosing Just Some Cases
  4. Self-Selection
  5. Missing What Matters
  6. Data Which Might Have Been
  7. Changes with Time
  8. Definitions of Data
  9. Summaries of Data
  10. Measurement Error and Uncertainty
  11. Feedback and Gaming
  12. Information Asymmetry
  13. Intentionally Darkened Data
  14. Fabricated and Synthetic Data
  15. Extrapolating beyond Your Data

If you look at the list and think that these types of dark data are not mutually exclusive, it is because these types of dark data are not mutually exclusive. Accordingly, the list does not add up and does not provide a unified framework to help the reader understand the various characteristics of dark data. To make matters worse, the overview of different types of dark data is not used to provide structure to the book or the chapters. On the contrary, the various types of dark data is introduced at various places in non-chronological order and some are used multiple times and in relation to other types of dark data. I guess this is to help the reader and show the complexities with the different types of dark data, but it is not working.

The reason I believe the book serves as a good introduction to various topics in statistics is that it uses several examples that you find in introductory statistics books, including the Simpson’s paradox, regression towards the mean, correlation and causation and measurement validity. Dark Data 9 (Summaries of Data), for example, is about how the average can hide important information (that’s as introductory as it gets).

The book is good at providing several great examples but does rarely focus on the actual solutions or potential tools that can help us work with “dark data” (except the awareness of such issues). While the book describes the different types of missing data (missing completely at random, missing at random and not missing at random), there is very little information on how to maximize the information we have available and minimize bias in relation to the different issues we encounter when working with “dark data”. I know it’s not the point of the book to deal with listwise deletion, imputation etc., but I would have liked to see the book deal a little more with the statistics of missing data.

In sum, it’s an OK book. If you are already familiar with the concepts above, you don’t need to read the book. However, if you have no to little experience with statistics, this book might serve as a good primer on several problems and issues we encounter when we use and analyse data.

Confusing and misleading terms in psychology

I was reading a couple of articles with examples of terms in psychological research that are either confusing, ambiguous or misleading. The two articles are Fifty psychological and psychiatric terms to avoid: a list of inaccurate, misleading, misused, ambiguous, and logically confused words and phrases (Lilienfeld et al. 2015) and 50 Differences That Make a Difference: A Compendium of Frequently Confused Term Pairs in Psychology (Lilienfeld et al. 2017).

While the two articles are written with an explicit focus on psychological research, I can recommend the articles for three reasons. First, the use of clear language is key to all aspects of scientific research. Even if you do not find the specific examples relevant (or disagree with some of the arguments), the articles can help you think about the clarity of the terms you apply in your own work.

Second, several of the examples are not domain-specific to psychology. A lot of the terms are related to research methods and statistics and can be considered great advice on how to communicate methods and statistics. For example, do not write “p = .000” but “p < .001”.

Third, psychological theories and explanations are used in a lot of social science research. For example, the first example on the list, “A gene for”, is also relevant for political science (see e.g. Two Genes Predict Voter Turnout by Fowler and Dawes 2008). Accordingly, due to the popularity of psychology in social science research more generally, the two articles are not only relevant for psychologists.

Here is the full list (100 examples in total, i.e. 50 examples in each article):

Inaccurate or misleading terms

  1. “A gene for”
  2. Antidepressant medication
  3. Autism epidemic
  4. Brain region X lights up
  5. Brainwashing
  6. Bystander apathy
  7. Chemical imbalance
  8. Family genetic studies
  9. Genetically determined
  10. God spot
  11. Gold standard
  12. Hard-wired
  13. Hypnotic trance
  14. Influence of gender (or social class, education, ethnicity, depression, extraversion, intelligence, etc.) on X
  15. Lie detector test
  16. Love molecule
  17. Multiple personality disorder
  18. Neural signature
  19. No difference between groups
  20. Objective personality test
  21. Operational definition
  22. p = 0.000
  23. Psychiatric control group
  24. Reliable and valid
  25. Statistically reliable
  26. Steep learning curve
  27. The scientific method
  28. Truth serum
  29. Underlying biological dysfunction

Frequently misused terms

  1. Acting out
  2. Closure
  3. Denial
  4. Fetish
  5. Splitting

Ambiguous terms

  1. Comorbidity
  2. Interaction
  3. Medical model
  4. Reductionism

Oxymorons

  1. Hierarchical stepwise regression
  2. Mind-body therapies
  3. Observable symptom
  4. Personality type
  5. Prevalence of trait X
  6. Principal components factor analysis
  7. Scientific proof

Pleonasms

  1. Biological and environmental influences
  2. Empirical data
  3. Latent construct
  4. Mental telepathy
  5. Neurocognition

Confused term pairs: Sensation, perception, learning, and memory

  1. “Negative reinforcement” versus “punishment”
  2. “Renewal effect” versus “spontaneous recovery”
  3. “Sensation” versus “perception”
  4. “Working memory” versus “short-term memory”

Confused term pairs: Social and cultural bases of behavior

  1. “Conformity” versus “obedience”
  2. “Prejudice” versus “discrimination”
  3. “Race” versus “ethnicity”
  4. “Sex” versus “gender”

Confused term pairs: Personality psychology

  1. “Affect” versus “mood”
  2. “Anxiety” versus “fear”
  3. “Empathy” versus “sympathy”
  4. “Envy” versus “jealousy”
  5. “Repression” versus “suppression”
  6. “Shame” versus “guilt”
  7. “Subconscious” versus “unconscious”

Confused term pairs: Psychopathology

  1. “Antisocial” versus “asocial”
  2. “Catalepsy” versus “cataplexy”
  3. “Classification” versus “diagnosis”
  4. “Delusion” versus “hallucination”
  5. “Obsession” versus “compulsion”
  6. “Psychopathy” versus “sociopathy”
  7. “Psychosomatic” versus “somatoform”
  8. “Schizophrenia” versus “multiple personality disorder”
  9. “Serial killer” versus “mass murderer”
  10. “Symptom” versus “sign”
  11. “Tangentiality” versus “circumstantiality”
  12. “Transgender” versus “transvestite”

Confused term pairs: Research methodology and statistics

  1. “Cronbach’s alpha” versus “homogeneity”
  2. “Discriminant validity” versus “discriminative validity”
  3. “External validity” versus “ecological validity”
  4. “Face validity” versus “content validity”
  5. “Factor analysis” versus “principal components analysis”
  6. “Predictive validity” versus “concurrent validity”
  7. “Mediator” versus “moderator”
  8. “Prevalence” versus “incidence”
  9. “Risk factor” versus “cause”
  10. “Standard deviation” versus “standard error”
  11. “Stepwise regression” versus “hierarchical regression”

Confused term pairs: Miscellaneous

  1. “Clairvoyance” versus “precognition.”
  2. “Coma” versus “persistent vegetative state”
  3. “Culture-fair test” versus “culture-free” test
  4. “Delirium” versus “dementia”
  5. “Disease” versus “illness”
  6. “Flooding” versus “implosion”
  7. “Hypnagogic” versus “hypnopompic”
  8. “Insanity” versus “incompetence”
  9. “Relapse” versus “recurrence”
  10. “Stressor” versus “stress”
  11. “Study” versus “experiment”
  12. “Testing” versus “assessment”

Do consult the articles for descriptions of the respective terms.

Tidyverse resources on YouTube

I have been watching a lot of YouTube videos lately with people using tidyverse. These videos are not tutorials per se but rather demonstrations on how to wrangle and analyse data. These videos use a lot of dplyr and ggplot2 as well as packages associated with the tidyverse, e.g. tidytext. For the data, they often use data associated with the TidyTuesday project.

I can highly recommend the following three channels: 1) David Robinson, 2) Julia Silge and 3) TidyX. The first two write their own code. All of them are good at going through various functions in R demonstrating the power of tidyverse.

The videos by David Robinson are great for beginners. He is good at introducing various packages and functions in a simple manner (or as simple as possible). In addition, you can find a systematic overview of the packages and functions introduced in the different videos here (made by Alex Cookson).

Of course, you can also – with little extra work – find other good videos on YouTube related to tidyverse, e.g. this video with 18 tips and tricks. The key benefit of videos on YouTube (compared to text guides) is that you can actually see how things are carried out and pick up on good/best practices. There is also an overview of other YouTube accounts here (though I am not yet familiar with all of them).

In brief, if you are already familiar with the basics of R but are looking for various videos on how to improve your data wrangling and visualisation skills, I can highly recommend these resources on YouTube.

Potpourri: Statistics #67

Computational Causal Inference at Netflix
Tools for Ex-post Survey Data Harmonization
How to pick more beautiful colors for your data visualizations
Shiny in Production: App and Database Syncing
Introduction to Causal Inference
An Illustration of Decision Trees and Random Forests with an Application to the 2016 Trump Vote
Key things to know about election polling in the United States
State-of-the-art NLP models from R
Introduction to Stan in R
How to write your own R package and publish it on CRAN
Bayesian Analysis for A/B Testing
Estimating House Effects
Heatmaps in ggplot2
The Taboo Against Explicit Causal Inference in Nonexperimental Psychology
Spreadsheet workflows in R
A beginner’s guide to Shiny modules
Dataviz Interview
10 Things to Know About Survey Experiments
Applying Weights
Creating effective interrupted time series graphs: Review and recommendations
Lasso and the Methods of Causality
How We Designed The Look Of Our 2020 Forecast
Taking Control of Plot Scaling
How to measure spatial diversity and segregation?
10+ Guidelines for Better Tables in R
How maps in the media make us more negative about migrants
Comparing two proportions in the same survey
Quantitative Social Science Methods, I (Gov2001 at Harvard University)
Introduction to Computational Thinking
Creating R Packages with devtools
Introduction to Statistical Learning in R
Textrecipes series: Pretrained Word Embedding

32

I should note that I write this for nobody but myself. Don’t expect anything interesting here. Feel free to skip this one. I promise there will be another post soon. This is – as always – a personal blog. My goal with this post is not to write anything that will transcend time and be relevant in the future. On the contrary, my hope is that I will read these words some day in the distant future and be unable to remember or even recognise my thoughts. Maybe that’s my biggest fear at the age of 32? To be complacent.

Yes, I am 32 now. It’s no big deal. That’s the thing you learn as you get older, I guess. I am 32 and one day in the (near) future being 32 will be nothing but a distant memory. That’s at least how I feel about being 16 (that it’s a distant memory of a time that, in the grand scheme of things, is nothing).

Time is weird. Life is weird. I was going through some old mails from 2007 the other day and I found a mail from my then-local now-closed library, Bagenkop Bibliotek. They informed me that a book I had requested had finally arrived. The book was ‘Mennesket er en misforståelse – portræt af Johannes Sløk’ by Kjeld Holm, and I don’t remember reading the book. Or seeing the book. It’s even limited what I know about the life of Johannes Sløk. I wonder how much I will remember about life now in 13 years time.

Ryan Holliday wrote a post titled 32 Thoughts From a 32-Year-Old. Inspired by that, I decided to write a blog post about my life when I am 32. It’s not a similar post and I do not agree with everything Ryan Holliday writes. For example, a ‘Top highlight’ in the post is: “You need a philosophy and you need to write it down. And re-write it and go over it regularly. Life is too hard (and too complicated) to try to wing it and expect to do the right thing.” I believe there is a contradiction at play. Life’s too hard and too complicated to have a philosophy. You don’t need a philosophy (or maybe I am wrong and Ryan Holliday is right). However, I agree wholeheartedly with some of the other observations, e.g. “Don’t read people’s long captions on Instagram. They are almost universally inane bullshit.”

The most recent significant change in my life is that I don’t have any academic ambitions anymore (if I ever had any). The more time you spend in academia the more you notice that a lot of your victories are Pyrrhic victories. One of the first thing you notice when you get into academia is also how obsessed people are with playing status games. It’s a blessing not having to care about metrics such as teaching evaluations, journal rankings, impact assessments, h-index, etc. I still care about research and science, but I have reached the conclusion that life’s too short to waste it in academia. My sense is that a lot of people are stuck in academic jobs they don’t like, but a weird combination of useless skills and opportunity costs keep them going strong. What I appreciate about non-academic jobs is the pace, energy and efficiency in which you get stuff done.

I like to multitask and devote time to several different ideas and projects. I rarely procrastinate but I do spend time on multiple tasks with no immediate pay-off. I know all of the benefits of deep work but I find the idea of deep work overrated. I believe most people are in love with the idea of deep work because they are addicted to social media. I am not as pessimistic towards social media. On the contrary, I believe that there are spillover effects of multitasking on various projects – and there is some empirical evidence supporting that claim (e.g. Kapadia and Melwani 2020). I did not like Tim Harford’s Messy: How to Be Creative and Resilient in a Tidy-Minded World but the main message of the book resonates a lot with how I work.

That’s also why I love Twitter. I have (so far) been able to beat the algorithm and have all content presented in the correct temporal order. I don’t want Twitter to present more tweets about American politics because I retweet a new forecast on the US presidential election. I want to be able to mix different Twitter accounts together in different lists. I like the juxtaposition you encounter on Twitter, e.g. that a tweet with a comment on Claude Monet’s late works can be followed by a tweet with a meme about heteroscedasticity-consistent standard errors.

I also try to increase the variation in the non-work related books I read, at least in terms of the topics. Some of the books I have read recently that I with a certain degree of confidence can say are not work-related include The Rise and Fall of the Dinosaurs (about the 200-million-year-long story of dinosaurs), The Monopolists (about the history of the board game Monopoly), Leonardo da Vinci (the one by Walter Isaacson) and Coffee Lids: Peel, Pinch, Pucker, Puncture (a beautiful book about disposable coffee lids).

The term ‘non-work related’ is technically correct but also fundamentally wrong. I believe that everything I do, even if it completely unrelated to my work, will matter in one way or another at some point in time. Or, in other words, if I only read books that were directly related to my job, I would not enjoy my job. There might not be a high rate of return to everything I do, but I believe it is the most efficient strategy for me. This is not the same as I don’t try to think systematically about procrastination and ways to improve my production function. I am familiar with various productivity methods and I find this overview by Alexey Guzey on how to think about productivity interesting. What I have learned over time is that I have no system, no method, no approach to being productive. I work a lot and find time to write (mostly text and code) every day. And as long as I am having fun and I am not feeling stressed, I’m sure everything will be fine (and I am aware that I might read that paragraph again at some point in the future and laugh).

2020 has also been a great year to reflect about planning – or rather the lack of planning. It’s strange living in the midst of a global pandemic and I don’t really have a lot to add here (see my post in Danish here). The only thing I should emphasise is how happy I am that I am not too affected by the situation and am able to work well from home.

I am also happy about living in a large city. We know that large cities both have pros and cons (e.g. both wages and crime scale in the same way with population size, cf. Bettencourt and West 2010) but from a productivity perspective, large cities are better as research, innovation and industry, concentrate disproportionately in large cities (cf. Balland et al. 2020). As Derek Sivers describes it: “One of the best things you can do for your career is to move to a big city”. That being said, what I like and appreciate the most about a large city is the anonymity. When I look at Edvard Munch’s ‘Aften på Karl Johan’, I don’t see alienation. I see peace and harmony.

As I get older I care more about my physical and mental health. I aim to go for a walk every day (on average), get enough sleep, not drink or smoke, drink decaffeinated coffee, avoid sugar and processed foods etc. I don’t use social media on my phone. The one thing that is not working out for me is playing squash. I haven’t played squash at all in 2020 and I miss that. Another important aspect of my mental health is culture. I try to have non-zero weeks in relation to most cultural activities, i.e. read a book, watch a movie, listen to a podcast, watch a TV show and listen to a music album every week. However, most important for my mental health is the great people I spend most of my time with.

Beyond that, I don’t really have a lot to say or any wisdom to give. That’s what I like about being 32. You’re old but not old enough to give any profound wisdom. Old enough to have learned a lot but young enough to still learn. If I could give three simple pieces of advice, they should most likely be: 1) Save a non-trivial amount of your after-tax income. 2) Give away the books you are never going to read. 3) Avoid flights before 11am and after 8pm (or, avoid flights at all in 2020 – taking the pandemic and the climate into account). Don’t take them too serious though. I am only 32.