Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.
For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.
That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range- an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.
Here are a few examples. First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.
If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.
Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.
Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.
Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.
Many thanks to Rosie Campbell for inspiration and discussion on this idea.
I spent a long time stalling on this post because I was framing the problem as “how to choose a book (or paper. Whatever)?”. The point of my project is to be able to get to correct models even from bad starting places, and part of the reason for that goal is that assessing a work often requires the same skills/knowledge you were hoping to get from said work. You can’t identify a good book in a field until you’ve read several. But improving your starting place does save time, so I should talk about how to choose a starting place.
One difficulty is that this process is heavily adversarial. A lot of people want you to believe a particular thing, and a larger set don’t care what you believe as long as you find your truth via their amazon affiliate link (full disclosure: I use amazon affiliate links on this blog). The latter group fills me with anger and sadness; at least the people trying to convert you believe in something (maybe even the thing they’re trying to convince you of). The link farmers are just polluting the commons.
With those difficulties in mind, here are some heuristics for finding good starting places.
Search “best book TOPIC” on google
Most of what you find will be useless listicles. If you want to save time, ignore everything on a dedicated recommendation site that isn’t five books.
If you want to evaluate a list, look for a list author with deep models on both the problem they are trying to address, and why each book in particular helps educate on that problem. Examples:
A bad list will typically have a topic rather than a question they are trying to answer, and will talk about why books they recommend are generically good, rather than how they address a particular issue. Quoting consumer reviews is an extremely bad sign and I’ve never seen it done without being content farming.
Look at highly cited papers. Even if they’re wrong, they’re probably important for understanding what else you read.
Look at what they cite or are cited by
Especially keep an eye out for review articles
Search for web forums on your topic (easy mode: just check reddit). Sometimes these will have intro guides with recommendations, sometimes they will have where-to-start posts, and sometimes you can ask them directly for recommendations. Examples:
Search Amazon for books on your topic. Check related books as well.
Ask your followers on social media. Better, announce what you are going to read and wait for people to tell you why you are wrong (appreciate it, Ian). Admittedly there’s a lot of prep work that goes into having friends/a following that makes this work, but it has a lot of other benefits so if it sounds fun to you I do recommend it. Example:
My goal was to come up with a system for reading a book. I eventually identified that as the wrong goal, but came up with a pretty great system for doing the much better goal of “how do I answer a question?” But developing that was not the hardest or most time consuming part of my research over the last 3 months (plus additional time working on covid). I feel weird talking about it, but the truth is, a lot of that time was spent overcoming emotional issues around learning.
For example, I think I’ve discussed before (but could not find a link on) how I kind of have two modes when reading: too credulous, looking for reasons a work could be true, and too antagonistic, looking for reasons to not only disagree, but dismiss entirely.
I introspected on this, and eventually figured out that at a deep level I felt I needed to believe books, that I was being bad if I disagreed with them. So of course I developed tools to prove my disagreements, which led to the bifurcation- either I was giving in to the original impulse or its counter, without the option of responsiveness.
This same block on challenging authority was behind my urge to start from a book rather than a question. I not only believed I needed to trust in an authority (as deemed by the publishing-industrial-complex) to give me answers, I needed to let them set the questions.
A natural question here is “why are you so sure this emotional work helped this specific task?” My evidence is how the needs-to-be-retitled-epistemic-spot-check project has evolved- I started out having books thrown at me and reading them with the goal of forming a yes-no verdict. I’ve now progressed to starting with a question (such as “What can the 1973 Oil Crisis tell us about supply shocks?”) that serves a specific purpose and finding work that advances it. In that post I derived a model from disparate pieces of information I sought out to answer specific questions. No books, no teachers, just me. I also have pretty extensive notes of the work I was doing and how it tracked to specific improvements, although they’re intensely personal and I will not be sharing them.
It’s not totally solved yet. I really wanted to read a book on the oil crisis, for exactly the reasons they’re a bad solution to the problem. I wanted someone to give me the answer. But I can at least see the desire for what it is, recognize that it’s not a desire to learn, and react appropriately.
Another natural question is “why does this happen?” There’s two answers to that- why did I specifically form that belief, and why did those circumstances have the power to make me form that belief. I have some guesses for the former; my failed state middle school where I was dependent on the goodwill of teachers for my physical safety is a top contender, although the “best” schools arguably use a more subtle stick to inculcate this attitude even more strongly.
For the latter, I have a very rough theory, dependent on the types of knowledge I described here. Very crudely, I believe trauma instills scientific-type knowledge that is factually false but locally adaptive. False beliefs need more protection to be maintained than true beliefs, so the belief both calcifies, making it unresponsive to new information, and lays a bunch of emotional landmines around itself to punish you for getting too close to it. This cascades into punishing you for learning at all, because you might learn something that corrects your false-but-useful model.
How did I escape these traps? I have some guesses for that, but I can only confidently identify the things I did immediately before breakthroughs. I’ve been building these skills for 10 years, so there’s a lot of background knowledge and skill feeding into that success I don’t have conscious access to. I think instructions for only the immediate predecessors of break throughs could be useless to outright harmful. So my next project is figuring out more about this process, and hopefully finding generalizable techniques for improvement.
Part of finding techniques that work on people other than me is talking to other people. If you’re interested in ways of contributing to or just using the knowledge, here are some options:
Already done this work? Tell me more. You can comment here, e-mail elizabeth@ this-domain.com, or use this anonymous form.
Want to hear ideas and try them out yourself, ideally reporting back to me? Sign up for this google group.
Want to devote several months of your life to working with me on this intensely? A number of things would have to go right for that work out, but if they all did, I think the potential is enormous. Email me at elizabeth @ this-domain
EDIT 7/15: Greetings again, Hacker News readers. This piece is the penultimate post in a long saga. If it strikes a chord with you, I’d encourage you check it out from the beginning, and check in in a few days for climax and epilogue. You can also follow me via RSS, via email by clicking the Follow button at the bottom right of the page, or on Twitter. Also while this post wasn’t on Patreon, many of mine are, and support is greatly appreciated. You can also Talk To Me For An Hour, although we’ll see how that stands up to the influx of new readers.
This is a system for sorting types of knowledge. There are many like it, but this one is mine.
First, there is knowledge you could regurgitate on a test. In any sane world this wouldn’t be called knowledge, but the school system sure looks enthusiastic about it, so I had to mention it. Examples:
Reciting the symptoms of childbed fever on command
Reciting Newton’s first law of motion
Reciting a list of medications’ scientific and brand names
Reciting historical growth rate of the stock market
Reciting that acceleration due to gravity on Earth is 9.807 m/s²
Second, there is engineering knowledge- something you can repeat and get reasonably consistent results. It also lets you hill climb to local improvements. Examples:
Knowing how to wash your hands to prevent childbed fever and doing so
Driving without crashing
Making bread from a memorized recipe.
What are the average benefits and side effects from this antidepressant?
Knowing how much a mask will limit covid’s spread
Investing in index funds
Knowing that if you shoot a cannon ball of a certain weight at a certain speed, it will go X far.
Knowing people are nicer to me when I say “please” and “thank you”
Third, there is scientific knowledge. This is knowledge that lets you generate predictions for how a new thing will work, or how an old thing will work in a new environment, without any empirical knowledge.
Understanding germ theory of disease so you can take procedures that prevent gangrene and apply them to childbed fever.
Knowing the science of baking so you can create novel edible creations on your first try.
Knowing enough about engines and batteries to invent hybrid cars.
Actually understanding why any of those antidepressants works, in a mechanistic way, such that you can predict who they will and won’t work for.
A model of how covid is spread through aerosols, and how that is affected by properties of covid and the environment.
Having a model of economic change that allows you to make money off the stock market in excess of its growth rate, or know when to pull out of stocks and into crypto.
A model of gravity that lets you shoot a rocket into orbit on the first try.
A deep understanding of why certain people’s “please”s and “thank you”s get better results than others.
Engineering knowledge is a lot cheaper to get and maintain than scientific knowledge, and most of the time it works out. Maybe I pay more than I needed to for a car repair; I’ll live (although for some people the difference is very significant). You need scientific knowledge to do new things, which either means you’re trying something genuinely new, or you’re trying to maintain an existing system in a new environment.
I don’t know if you’ve noticed, but our environment was changing pretty rapidly before a highly contagious, somewhat deadly virus was released on the entire world, and while that had made things simpler in certain ways (such as my daily wardrobe), it has ultimately made it harder to maintain existing systems. This requires scientific knowledge to fix; engineering won’t cut it.
And it requires a lot of scientific knowledge at that- far more than I have time to generate. I could trust other people’s answers, but credentials and authority have never looked more useless, and identifying people I trust on any given subject is almost as time consuming as generating the answers myself. And I don’t know what to do about that.
This will be kind of meandering, so here’s the take home.
For shallow research:
Determine/discover what you care about before you start reading.
Write down anything relevant to that care.
For deep research:
Write down anything you find interesting.
Write down anything important to the work’s key argument.
Write down anything that’s taking up mental RAM, whether it seems related or interesting or not. If you find you’re doing this a lot, consider you might have a secret goal you don’t know about.
The less 1:1 the correspondence between your notes and the author’s words the better. Copy/pasting requires little to no engagement, alternate theories for the explanations spread over an entire chapter require a lot.
Now back to our regularly scheduled blog post.
Writing down a thing you’ve read (/heard/etc) improves your memory and understanding, at the cost of disrupting the flow of reading. Having written a thing down makes that one thing easier to rediscover, at the cost of making every other thing you have or will ever write down a little harder to find. Oh, and doing the math on this tradeoff while you’re reading is both really costly and requires knowing the future.
I would like to give you a simple checklist for determining when to save a piece of information. Unfortunately I never developed one. There are obvious things like “is this interesting to me (for any reason)?” and “is this key to the author’s argument?”, but those never got rid of the nagging feeling that I was losing information I might find useful someday, and specifically that I was doing shallow research (which implies taking the author’s word for things) and not deep (which implies making my own models).
The single most helpful thing in figuring out what to write down was noticing when my reading was slowing down, which typically meant either there was a particular fact that needed to be moved from short to long term storage, or that I needed to think about something. Things in these categories need to be written down and thought about regardless of their actual importance, because their perceived importance is eating up resources, and 30 seconds writing something down to regain those resources is a good trade even if I never use that information again. If I have one piece of advice, it’s “learn to recognize the subtle drag of something requiring your attention.”
An obvious question is “how do I do that though?”. I’m a mediocre person to answer this question because I didn’t set out to learn the skill, I just noticed I was doing it. But for things in this general class, the best thing I have found to do is get yourself in a state where you are very certain you have no drag (by doing a total brain dump), do some research, and pay attention to when drag develops.
But of course it’s much better if my sense of “this is important, record it” corresponds with what is actually important. The real question here is “Important to what?” When I was doing book-based reviews, the answer at best was “the book’s thesis”, which as previously discussed gives the author a huge amount of power to control the narrative. But this became almost trivial when I switched the frame to answering a specific set of questions. As long as I had a very clear goal in mind, my subconscious would do most of the work.
This isn’t a total solution though, because of the vast swath of territory labeled “getting oriented with what I don’t know”. For example right now I want to ask some specific questions about the Great Depression and what it can tell us about the upcoming economic crisis, but I don’t feel I know enough. It is very hard to get oriented with patchwork papers: you typically need books with cohesive narratives, and then to find other ways to undo the authors’ framing. Like a lot of things, this is solved by going meta. “I want to learn enough about the Great Depression that I have a framework to ask questions about parallels to the current crisis” was enough to let me evaluate different “Top Books about the Great Depression” lists and identify the one whose author was most in line with my goals (it was the one on fivebooks, which seems to be the case much more often than chance).
I mentioned “losing flow” as a cost of note taking in my opening, but I’m not actually convinced that’s a cost. Breaking flow also means breaking the author’s hold on you and thinking for yourself. I’ve noticed a pretty linear correlation between “how much does this break flow?” and “how much does this make me think for myself and draw novel conclusions?”. Copy/pasting an event that took place on a date doesn’t break flow but doesn’t inspire much thought. Writing down your questions about information that seems to be missing, or alternate interpretations of facts, takes a lot longer.
Which brings me to another point: for deep reading, copy pasting is almost always Doing It Wrong. Even simple paraphrasing requires more engagement than copy/pasting. Don’t cargo cult this though: there’s only so many ways to say simple facts, and grammar exercises don’t actually teach you anything about the subject.
So there is my very unsatisfying list of how to know what to write down when you’re reading to learn. I hope it helps.
When I began what I called the knowledge bootstrapping project, my ultimate goal was “Learn how to learn a subject from scratch, without deference to credentialed authorities”. That was too large and unpredictable for a single grant, so when I applied to LTFF, my stated goal was “learn how to study a single book”, on the theory that books are the natural subcomponents of learning (discounting papers because they’re too small). This turned out to have a flawed assumption baked into it.
As will be described in a forthcoming post, the method I eventually landed upon involves starting with a question, not a book. If I start with a book and investigate the questions it brings up (you know, like I’ve been doing for the last 3-6 years), the book is controlling which questions get brought up. That’s a lot of power to give to something I have explicitly decided not to trust yet.
When reading The Unbound Prometheus, I took the book’s word that a lower European birth rate would prove Europeans were more rational than Asians and focused on determining whether Europe’s birth rates were in fact lower (answer: it’s complicated), when on reflection it’s not at all clear to me that lower birth rates are evidence of rationality.
“Do humans have exactly 4 hours of work per day in them?” is not actually a very useful question. What I really wanted to know is “when can I stop beating myself up for not working?“, and the answer to the former doesn’t really help me with the latter. Even if humans on average have 4 hours, that doesn’t mean I do, and of course it varies by circumstances and type of work… and even “when can I stop beating myself up?” has some pretty problematic assumptions built into it, such as “beating myself up will produce more work, which is good.” The real question is something like “how can I approach my day to get the most out of it?”, and the research I did on verifying a paper on average daily work capacity didn’t inform the real question one way or the other.
What would have been better is if I’d started with the actual question I wanted to answer, and then looked for books that had information bearing on that question (including indirectly, including very indirectly). This is what I’ve started doing.
This can look very different depending on what type of research I’m doing. When I started doing covid research, I generated a long list of fairly shallow questions. Most of these questions were designed to inform specific choices, like “when should I wear what kind of mask?” and “how paranoid should I be about people without current symptoms?”, but some of them were broader and designed to inform multiple more specific questions, such as “what is the basic science of coronavirus?”. These broader, more basic questions helped me judge the information I used to inform the more specific, actionable questions (e.g., I saw a claim that covid lasted forever in your body the same way HIV does, which I could immediately dismiss because I knew HIV inserted itself your DNA and coronaviruses never enter the nucleus).
I used to read a lot of nonfiction for leisure. Then I started doing epistemic spot checks– taking selected claims from a book and investigating them for truth value, to assess the book’s overall credibility- and stopped being able to read nonfiction without doing that, unless it was one of a very short list of authors who’d made it onto my trust list. I couldn’t take the risk that I was reading something false and would absorb it as if it were true (or true but unrepresentative, and absorb it as representative). My time spent reading nonfiction went way down.
About 9 months ago I started taking really rigorous notes when I read nonfiction. The gap in quality of learning between rigorous notes and my previous mediocre notes was about the same as the gap between doing an epistemic spot check and not. My time spent reading nonfiction went way up (in part because I was studying the process of doing so), but my volume of words read dropped precipitously.
And then three months ago I shifted from my unit of inquiry being “a book”, to being “a question”. I’m sure you can guess where this is going- I read fewer words, but gained more understanding per word, and especially more core (as opposed to shell or test) understanding.
The first two shifts happened naturally, and while I missed reading nonfiction for fun and with less effort, I didn’t feel any pull towards the old way after I discovered the new way. Giving up book-centered reading has been hard. Especially after five weeks of frantic covid research, all I wanted to do was to be sat down and told what questions were important, and perhaps be walked through some plausible answers. I labeled this a desire to learn, but when I compared it to question-centered research, it became clear that’s not what it was. Or maybe it was a desire to go through the act of learning something, but it was not a desire to answer a question I had and was not prioritized by the importance of a question. It was best classified as leisure in the form of learning, not resolving a curiosity I had. And if I wanted leisure, better to consume something easier and less likely to lead me astray, so I started reading more fiction, and the rare non-fiction of a type that did not risk polluting my pool of data. And honestly I’m not sure that’s so safe: humans are built to extract lessons from fiction too.
Put another way: I goal factored (figured out what I actually wanted from) reading a nonfiction book, and the goal was almost never best served by using a nonfiction book as a starting point. Investigating a question I cared about was almost always better for learning (even if it did eventually cash out in reading a book), and fiction was almost always better for leisure, in part because it was less tiring, and thus left more energy for question-centered learning when that was what I wanted.
I’ve been known to accuse people who say open offices are “fine with a few mitigations” of not paying attention to the cost of their mitigations. I believed they shrunk their thoughts down to the point that not much was lost from an interruption, at the cost of only being able to think the thoughts that fit in that interval. Any thought that would take too long to process could not be conceived of.
I’ve also been known to accuse people who advocate for deep, uninterrupted work without the distractions of social media of “not understanding how valuable social media is to me”. And besides, my workflow works best with frequent breaks (that I choose the timing of) because I “background process”.
I maintained this illusion until, inspired by a stupidly expensive device that only does one thing, I taped my old phone to a bluetooth keyboard* and began to write in offline mode. It was immediately a magical experience. It was so *quiet*. I could go on my porch and write and it was quiet. My thoughts got much larger because I wasn’t subconsciously afraid I’d interrupt them. I began to feel angry at my laptop. Why did it insist on hurting me so much? Why couldn’t it be pure like the offline phone/keyboard experience? Why couldn’t I just create things?
[* I only found two bluetooth keyboards with an inlay for phones/tablets. The other one lacks a built in battery, and shipped with a broken key]
Locally, this lasted for about 10 minutes before the social media cravings kicked in. But that was enough. I deeply resented work for taking me away from my magic writing device and making everything so noisy.
Since I started, my desire for using the quiet device has waxed and waned. At first I thought this was reflective of some deep pathology, but after two weeks it looks a lot more like “sometimes the benefits of quiet outweighs the benefits of being able to look stuff up, sometimes they don’t’”. I’ve also changed how I interact on a connected device- I’m more likely to close Signal, less likely to open Twitter. This is less due to a utilitarian calculation of the costs and benefits of Twitter, and more that once I’m in a good state, I can notice how switching to Twitter is almost physically painful.
The problem is that I wasn’t wrong that social media was genuinely very valuable to me, and that was before we were all locked inside. But I definitely was wrong that getting those benefits were costless, in a way very analogous to mistakes I accused others of making. I’m glad I have the information now, but I haven’t figured out what to do with it yet.
Last month I investigated commonalities between recessions of the last 50 years or so. But of course this recession will be different, because (among other things) we will simultaneously have a labor shortage and a lot of people out of work. That’s really weird, and there’s almost no historical precedent- the 1918 pandemic took place during a war, and neither 1957 nor 1968 left enough of an impression to have a single book dedicated to them.
So I expanded out from pandemics, and started looking for recessions that were caused by any kind of exogenous shock. The best one I found was the 1973 Oil Crisis. That was kicked off by Arab nations refusing to ship oil to allies who had assisted Israel during the Yom Kippur war- as close as you can get to an economic impact without an economic cause. I started to investigate the 1973 crisis as the one example I could find of a recession caused by a sudden decrease in a basic component of production, for reasons other than economic games.
Spoiler alert: that recession was not caused by a sudden decrease in a basic component of production either.
Why am I so sure of this? Here’s a short list of little things,
A multiyear stock market crash started in January 1973, 9 months before embargo was declared.
Previous oil embargoes had been attempted in 1956 and 1967, to absolutely no effect.
But here’s the big one: we measure the price of oil in USD. That’s understandable, since oil sales are legally required to be denominated in dollars. But the US dollar underwent a massive overhaul in 1971, when America decided it was tired of some parts of the Bretton Woods Agreement. Previously, the US, Japan, Canada, Australia and many European countries maintained peg (set exchange rate) between all other currencies and USD, which was itself pegged to gold. In 1971 the US decided not to bother with the gold part anymore, causing other countries to break their peg. I’m sure why we did this is also an interesting story, but I haven’t dug into it yet, because what came after 1971 is interesting enough. The currency of several countries appreciated noticeably (Germany, Switzerland, Japan, France, Belgium, Holland, and Sweden)…
(I apologize for the inconsistent axes, they’re the best I could do)
…but as I keep harping on, oil prices were denominated in dollars. This meant that oil producing countries, from their own perspective, were constantly taking a pay cut. Denominated in USD, 1/1/74 saw a huge increase in the price of oil. Denominated in gold, 1/1/74 saw a return to the historic average after an unprecedented low.
(apologies for these axes too- the spike in this graph means oil was was worth less, because you could buy more with the same amount of gold)
This is a little confusing, so here’s a timeline:
1956: Failed attempt at oil embargo
1967: Failed attempt at oil embargo
1971, August: US leaves the gold standard
1972: Oil prices begin to fall, relative to gold
1972, December: US food prices begin to increase the rate of price increases.
1973, January: US Stock market begins 2-year crash
1973, August: US food prices begin to go up *really* fast
1973, October, 6: Several nearby countries invade Israel
1973, October, 17: Several Arab oil producing countries declare an embargo against Israeli allies, and a production decrease. Price of oil goes up a little (in USD).
1974, January, 1: Effective date of declared price increase from $5.12 to $11.65/barrel. Oil returns to historically normal price measured in gold.
This is not the timeline you’d expect to see if the Yom Kippur war caused a supply shock in oil, leading to a recession.
My best guess is that something was going wrong in the US and world economy well before 1971, but the market was not being allowed to adjust. Breaking Bretton Woods took the finger out of the dyke and everything fluctuated wildly for a few years until the world reached a new equilibrium (including some new and different economic games).The Yom Kippur war was a catalyst or excuse for raising the price of oil, but not the cause.
Thanks to my Patreon subscribers for funding this research, and several reviewers for checking my research and writing.
Long time readers may remember when I endorsed Kencko, a collection of freeze dried fruit and vegetable powders in convenient packaging. I was extremely excited about Kencko, because I have various food issues that lead to not eating enough produce, freeze drying maintains most nutritional value, and powders are really easy for me. Or, they usually are. For reasons I never figured out, Kencko made me vomit. I looked around for better but couldn’t find any, and dropped it.
My interest in produce powders was re-aroused recently by concerns about the food supply chain in the US. I don’t think we’re going to starve, but I do think produce variety will be severely curtained, price will go up, and more produce will be processed rather than fresh or frozen (because harvest for processing requires less manpower). Shelf-stable produce that didn’t take up much space was suddenly attractive.
Was exactly what I wanted, in terms of being 100% freeze dried and having a variety of ingredients. I looked really hard for this and ENOF was the only thing that fit both requirements.
Didn’t make me throw up (I waited to write this endorsement specifically to test that).
Came in a convenient cinnamon-shaker like container. It is embarrassing how much more I use this powder because it is a convenient shaker rather than requiring a spoon to dish out. I combined the contents of two containers to create an empty one I could put my protein powder in, and now I’m using that more too.
Tastes fine for what it is.
If you are interested, I have a 25% off code here: SBHBJZGJ, which you will need, because this product is not cheap. This is not a referral code, I don’t get any money for you using it, I’m not even sure it will work for more than one person because it was sent to me personally (immediately after I made a large purchase ). I honestly thought about not publishing this because I don’t want them to run out, but I decided the risk of them going out of business due to insufficient demand was higher that I would be prosocial and share.
A few years/weeks ago, my boyfriend asked me “what happens during a recession?”, and I realized that while I knew the technical definition- GDP shrinks, unemployment grows- I didn’t really know what the human effects were. So as part of my temp job as “something something coronavirus” at LessWrong, I went looking.
All data is for the United States unless otherwise specified.
Unemployment increases in a recession. This creates a long lasting negative effect on people who enter the labor force during a recession (unemployment scarring).
From 1953-1980, women have a higher unemployment rate than men, during both expansions and recessions. From 1980 on, men and women have nearly identical unemployment rates in good times, but men’s unemployment has higher peaks during recessions.
Unemployment over time, by gender
At first I thought this was because men are more likely to work in manufacturing, which is more procyclic (see next section), but the pattern holds even within sectors
But unemployment typically means ‘is looking for work’. Perhaps women who lose their jobs are more likely to call themselves Stay-At-Home-Parents and stop looking for work. What happens to the labor force participation rate?
So that’s not it either.
Black and Latino people typically have a higher unemployment rate than white people (I did not find equivalent data for Asians over a long enough time period), and are hit harder during recessions
Unfortunately I could only find comparative data going back to 1990, but it looks like youth unemployment is continually higher than older adults, by a similar amount over time.
This isn’t the whole story though, because unemployment can have long term negative effects when you’re young, and especially when you’re just entering the workforce. This is known as unemployment scarring. People who enter the workforce during recessions have lowered employment, wages, and job fit, an effect that lasts for at least 15 years and possibly more. Herearesomepaperscovering the effect. Obviously the longest term data is available only for older recessions, and I could imagine that things aren’t as bad now given the loss of the one-employer-for-life model… but here’s one paper covering the Great Recession that says it’s still quite bad.
My prediction: divorces are postponed during a recession, leading to an apparent drop and then catch-up bounce
Reality: Trends in divorce continue basically unabated
This paper, the only long-time-scale survey I could find, reports a minor negative correlation between unemployment rates and divorce. However looking at their graph, the relationship is obviously mild.
My prediction: religious participation increases during a recession.
Reality: Religious Service Attendance Stays Flat
I was really surprised to find only one academic paper in the last 40 years on religiosity and economic conditions, which was not available online. It reports a “strong” countercyclic effect in religious participation in evangelical Protestants but procyclic effect in mainline Protestants, in the 2001 recession. Meanwhile a Pew poll and a Gallup poll show no change in religious participation during the 2008 recession.
I googled this before making a prediction, but do not believe I would have predicted the results.
People die a little less often, especially in nursing homes.
Deaths go down during recessions; according to Ruhm 2002, a 1% decrease in the unemployment rate is associated with an average 0.4% rise in total mortality (about 13,000 deaths, relative to the average of ~2.8m). This is counterintuitive, because wealth is associated with longevity (e.g. Chetty et al. 2016) . There were a lot of potential explanations for this centering on how work was dangerous and didn’t leave time for health, but it turns out most of the additional deaths are concentrated among groups that were unlikely to be employed in the first place, such as those over 70 (70% of the total) or under 4. Fewer than 10% of the additional deaths occur among those between the ages of 25 and 64 (Stevens et al 2011).
Why does employment of working-age adults have such an impact on elderly mortality? Stevens et almake a compelling case that it’s because widespread unemployment increases the relative number of people willing to take unpleasant, low-paying nursing home jobs, particularly entry level “aide” positions, and this improves care of residents.
My prediction: recessions lead to a moderate drop in number of live births
The effect of economic downturns on births is surprisingly complicated. On one hand, people have less money and kids are expensive*, which you would expect to lead to fewer children. On the other hand, a reduction in employment expectations reduces the opportunity cost of children, which you would expect to lead to more.
Based primarily on Economic recession and fertility in the developed world and spot checking its sources, my conclusion is that modern recessions temporarily decrease per capita births, but by and large do not change cohort fertility (i.e. women have the same number of total children they would have had without the recession, but later). Some trends:
The reduction in births is seen mostly in younger women (20-24), not older women (30-34), suggesting this is a voluntary decision incorporating knowledge of ability to have children in the future.
The effect is much larger for first births than subsequent births, suggesting this may be more about union formation than post-union decisions to have children (this could also explain the age-related effects)
The change seems to be driven more by change in situation than by absolute status, i.e. there isn’t a strict relationship between per capita GDP or unemployment and fertility that holds across countries, but in countries where children and women have the same status, people will react similarly to a change in circumstance.
Male unemployment is universally bad for fertility.
Female unemployment depends on the era (used to be positively associated with fertility, now is negatively) and on a woman’s socioeconomic status (richer/better educated women’s fertility is more procyclic than poorer/worse education women’s).
Generous unemployment insurance or non-employment-linked maternity benefits unsurprisingly raise the birth rate during a recession.
Specific numbers are hard to give because every country, demographic, and recession is different, but as an example, this article estimates ~9% decrease in fertility in 2013 in the US.
* This is in societies where children are economic sinks. In situations where they are assets, you would expect the reverse.
Thanks to Eli Tyre for research assistance on this section.
My prediction: Suicide rises in a recession
Reality: Suicide rates rise, primarily in unemployed men
31 of them found a positive association between economic recession and increased suicide rates.
2 studies reported a negative association,
2 articles failed to find any association
3 studies were inconclusive.
Unfortunately they didn’t share the effect size for most of these studies. Looking at other sources (notes here), I found anywhere from a 4% increase (across Europe and the Americas during the 2008 recession) to 60% (among men in Russia during the 1991 crisis). Studies typically found a much larger effect in men than women, sometimes finding no change in the female suicide rate at all. Different studies found different effects on different age groups; these felt too subdivided to me and I ignored them. Unsurprisingly, unemployment was positive correlated with suicide.
That 60% increase in Russia corresponded to an additional 30 deaths per 100,000 people per year, at a time when the overall death rate was 1300 deaths per 100,000 people. That 4% Europe/Americas increase represents 5000 deaths total, across three continents.
I was 3 for 5 on predictions, 3 for 6 if you include the one I didn’t formally predict ahead of time.