Gallup has polled Americans for many decades about their smoking habits. About 40-45% of adults smoked cigarettes from about 1945-1975, but the percentage has dropped steadily since then. A 2022 poll showed a new low of 11% being smokers. Roughly three in 10 nonsmokers say they used to smoke.
On the other hand, marijuana usage has climbed steadily since Gallup first asked about it in 1969. Some 16% of Americans say they currently smoke marijuana, while a total of 48% say they have tried it at some point in their lifetime:
Younger adults (18-34) are much more likely to be current users, but the 55+ crowd tried it nearly as much (44%) as the younger cohorts:
Among all adults, opinion is about evenly split on whether marijuana has a positive or negative effect on society and on people who use it. However, opinion is skewed very positive among those who have actually tried it, and negative among those who have not:
(I can’t resist inserting a consistent anecdotal observation by reliable people I know or know of, that habitual smoking of MJ tends to be highly correlated with passivity / lack of initiative, especially among young men. When one young man I know of told his counselor, “Nothing happens [when I smoke weed]”, the response was, “That’s the problem, nothing happens [because with weed you just chill and don’t do the stuff you need to do].” Of course, correlation says nothing about the direction of causation here).
The big gorilla of substance usage is still alcohol. About 45% of Americans have had an alcoholic drink within the past week, while another 23% say they use it occasionally. Alcohol use has remained relatively constant over the years. The average percentage of Americans who have said they are drinkers since 1939 is 63%, which is close to Gallup’s most recent reading of 67%.
Financial discussions often give the disclaimer “this is not investment advice” for legal reasons. I would always see this and wonder, is anyone ever willing to say “this *is* investment advice”?
I decided to take the Series 65 because I thought it would be a good learning opportunity, that it could be fun to tell people “this is investment advice”, and because it also provides the fast track to becoming an accredited investor. I’d like to have the option to invest in startups or hedge funds, but the SEC doesn’t let people do that unless they are rich (consistently over $300k/yr HH income, or $1mil in assets) or a licensed financial professional. I didn’t want to wait years to pass the income or asset tests, and so decided to pass the literal test instead.
I hoped that as a PhD economist who sometimes reads about finance for fun, I could pass the Series 65 without studying. This turned out not to be true, but it also wasn’t wildly wrong. You need to get at least 72% of questions correct to pass; taking a practice test cold I got 62%. I decided to first take the slightly easier Security Industry Essentials exam as a warmup. For both exams, I passed after spending ~ 2 weeks reading through the ~500 page study guides from the Securities Institute of America in my spare time.
For someone with an economics background, the exams will feature a few true econ questions you’ll know cold, a lot of “common sense” finance questions you probably know, some more specific finance questions you probably don’t know, and some specific questions about laws and regulations for investment advisers you almost certainly don’t know. This means you can speed through some parts of the study guide, but will need to slow way down in others. I found myself learning a roughly equal mix of things I’m happy to know for their own sake, things that would only be helpful to the extent I actually work as an investment adviser, and things that seem completely pointless.
Overall this seems like a decent way to spend a bit of time and money. Economists love to complain about people asking us for financial advice, and tend to either say “I don’t know, that’s not what economics is about” or give uninformed answers. But it doesn’t take that much time to educate yourself enough to be able to give people good, informed answers, so I think we should do so, especially when the alternatives people turn to tend to either be uninformed (friends or internet randos) or biased (advisers who get paid for steering them to high-fee investments).
That said, if your goal is actually to make money as an adviser or as an accredited investor, the Series 65 exam is only the first hoop to jump through. You still need to get licensed, which means either starting an investment advisory firm or joining one. I haven’t tried to do this yet despite passing the Series 65 in June, as I’ve been busy with my main job. I’d be interested to hear from anyone who has done this, especially anyone who got a part-time or consulting role just to get licensed to make accredited investments. How hard was it, how long did it take, what did you think of the actual work?
In a world where China and India continue to build huge, CO2-belching coal power plants, and a world where global supply chains can no longer be taken for granted, you might think that a small, crowded country like the Netherlands would prioritize home-grown food production over concerns about greenhouse gas emissions from a relatively small volume of cow manure. But this is Europe, the land of eco-utopianism, and so you would be wrong.
Cow poop does emit nitrous oxide (a greenhouse gas) and ammonia (which can potentially pollute local water if uncontained). In a burst of green virtue, the Netherlands has, “unveiled a world-leading target to halve emissions of the gasses, as well as other nitrogen compounds that come from fertilizers, by 2030, to tackle their environmental and climate impacts.” This target is expected to result in a 30% reduction in livestock numbers and the closure of many farms. Dutch farmers are not amused, and have vented their ire by dumping hay bales on highways and smearing manure outside the home of the agricultural minister. Protests over green policies hobbling local farmers have spread to Germany and Canada.
All this raised in my mind the question, could we really get along with using much less nitrogen-based fertilizers? I found a great article by Hannah Ritchie on OurWorldinData.org, “Can we reduce fertilizer use without sacrificing food production?”, which provides lush tables and graphs on the subject.
First, it’s estimated that artificial nitrogen fertilizers (where hydrogen, mainly derived from natural gas, is reacted with atmospheric nitrogen at high pressure over catalysts to make ammonia and derivatives) allow the world’s population to be about twice as high is it would be otherwise. Put another way, take away nitrogen fertilizers, and half of us die. So any campaign to massively scale back on fertilizer usage would result in mass starvation. You first…
That said, Ritchie’s article pointed out that some countries such as China seem to be (inefficiently) using much more fertilizer than they need to get similar results, some countries (e.g. America) seem to be about in balance, and some areas (e.g. sub-Saharan Africa) would benefit from using more fertilizer. So globally we could probably use a bit less fertilizer if the profligate countries used (a lot) less, while the deprived countries used a little more.
I’ll conclude with two charts from Ritchie’s article. The first chart shows, for instance, that Brazil uses twice as much fertilizer per hectare or per acre as the U.S, and China uses three times as much, while Ghana uses about a tenth as much.
The second chart shows estimated nitrogen use efficiency (NUE). An NUE of 40%, for instance, shows that 40% of the nitrogen in the fertilizer is converted to nitrogen in the form of crops, while the other 60% of the nitrogen becomes pollutants. In China and India, only about a third of the applied nitrogen is fully utilized, compared to two thirds in places like the U.S. and France. ( Some countries have a very high NUE – greater than 100%. This means they are undersupplying nitrogen, but continue to try to grow more and more crops. Instead of utilizing readily available nutrients, crops have to take nitrogen from the soil. Over time this depletes soils of their nutrients which will be bad for crop production in the long-run).
Last month Eric Basmajian published “Why Demographics Matter More Than Anything (For The Long Term)” on the financial site Seeking Alpha. He predicts that that the developed world plus China face a future of low economic growth (regardless of policy machinations) due simply to demographics. His key points:
Demographics are the most important factor for long-term analysis.
The young and old age cohorts negatively impact economic growth.
The prime-age population (25-64) drives the bulk of economic activity.
The world’s major economies are suffering from lower population growth and an older population.
Over the long run, the world’s major economies will have worse economic growth, which will negatively impact pro-cyclical asset prices (like stocks).
I will paste in some of his supporting charts. First, the labor force is more or less proportional to the 25-64 age cohort (U.S. data shown) :
…and GDP growth trends with labor force growth:
Also, on the consumption side, that is highest with the 25-54 age group:
And so,
Younger people are a drag on economic growth and older people are a drag on economic growth… The prime-age population is the segment that drives economic activity, so if the share of population that is 25-54 is shrinking, which it is, then you’re going to have more people that are a negative force than a positive force:
Once the working-age population growth flips negative, an economy is doomed…. Working age population growth in Japan flipped negative in the 1990s, and they moved to negative interest rates, QE, and they have never been able to stop. The economy is too weak.
After 2009, the working-age population in Europe flipped negative, and they moved to negative rates and QE, and they haven’t been able to stop. Even now, as the US is raising rates, Europe is struggling to catch up and has already abandoned most of its tightening plans.
In 2015, China’s working-age population flipped negative, and they’ve had problems ever since. They devalued their currency in 2015 and tried one more time to inflate a property bubble, but it didn’t work, and now they’re having to manage the deflation of an asset bubble that the population cannot support.
The US is in better shape than everyone else, but we’re not looking at robust growth levels in this prime-age population.
In conclusion, “ The real growth rate in most developed nations is collapsing because of those two factors, worsening demographics, and increased debt burdens. In the US, as a result of the demographic trends I just outlined plus a rising debt burden, real GDP per capita can barely sustain 1% increases over the long run compared to 2.5% in the 60s, 70s, and 80s.”
That is pretty much where Basmajian leaves it. No actionable advice (besides subscribing to his financial newsletter). What isn’t addressed is whether productivity (production per worker) can somehow be accelerated. Also, one of his charts (which I did not copy here) showed a big trend down in 25-64 age fraction in the US population in the 1950’s-1960’s (as hangover from the Depression?), and yet these were decades of strong GDP growth. So these demographic trends are not the whole story, but his analysis is sobering.
If you are trying to pick a career, it would help to know what the daily experience is like in various professions.
A friend of mine recently quit her old job and did a coding bootcamp. She worked hard, went through interviews and is now working in tech. She was correct in expecting that coding is more interesting and provides more opportunity than her old job.
She is not at a FAANG or grinding at a startup. She got hired in a remote position that requires an understanding of code. She’s starting at the bottom of the hierarchy in her 30’s, as someone with no experience.
Now that she has started work in the industry, she reported to me that, “I don’t think I could have predicted that the people would be this much fun.”
She is genuinely enjoying tech culture. She texts me obscure tech jokes now as if it’s an SNL skit that I would enjoy. (e.g. https://www.youtube.com/watch?v=kHW58D-_O64 somewhat obscure YouTube channel) Her previous job was boring, and she never told me a positive thing about it. She is happy, not just with her financial return on investment but with her community.
If you read much about tech policy, you have heard about harassment in the workplace, especially for women. This is indeed an important issue. I’m not presenting my anecdote to imply that everything is fine everywhere. If people are trying to make important life decisions, then this is worth discussing.
One factor that might make people not want to learn to code is that they are afraid the work would be isolating and boring. It can be, but there is also a community aspect that can be positive.
I polled my Twitter friends and got this result (small, biased sample, albeit, and I suspect it’s mostly men who answered):
No one disputed that tech folk can be fun, although some people wanted to qualify the statement by saying that different companies have different cultures.
John Vandevier (@JohnVandivier) sent me a blog he wrote about a study on tech culture. “Analyzing ‘Resetting Tech Culture’ by Accenture and Girls Who Code” The study shows that the world is complex. Lots of women are happy in tech. At the same time there are people who face harassment. There is good news and bad news. Offenders should stop offending. There are also good opportunities out there for people who train for tech.
When I shared the story about my friend’s good news, it was mostly ignored on Twitter. Good news does not drive engagement. Happy people are not interesting and so no one hears about them. Tech is not the right choice for everyone, and some people have been mistreated at tech companies, but on the margin a few more people should probably go for it.
Here’s something to balance out my rosy report about all the laughing and LOLing among coders. Last year I had a miserable long day of coding. I wrote up a diary entry about how much I hated that day. I’m not trying to get sympathy for myself. I wanted to capture a modern experience that is shared by many.
Coding can be hard and frustrating and lonely. The jokes are funny because the pain is real.
Science keeps getting bigger- more researchers, more funding, and of course more publications. Scientific progress is much harder to measure, but there are good arguments that it’s roughly flat over time. This implies that productivity per researcher is plummeting.
There’s been a lively debate about what drives this falling productivity- is it that the easy discoveries got made first, leaving only harder ones for today’s scientists? Or is something else tanking scientific productivity, like the bureaucratic way we organize scientific research today?
A recent paper, “Slowed canonical progress in large fields of science“, suggests that the growth in the number of researchers and publications could itself be part of the problem. Comparing scientific fields over time, they find that:
When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon.
What is driving this? They argue:
First, when many papers are published within a short period of time, scholars are forced to resort to heuristics to make continued sense of the field. Rather than encountering and considering intriguing new ideas each on their own merits, cognitively overloaded reviewers and readers process new work only in relationship to existing exemplars. A novel idea that does not fit within extant schemas will be less likely to be published, read, or cited. Faced with this dynamic, authors are pushed to frame their work firmly in relationship to well-known papers, which serve as “intellectual badges” identifying how the new work is to be understood, and discouraged from working on too-novel ideas that cannot be easily related to existing canon. The probabilities of a breakthrough novel idea being produced, published, and widely read all decline, and indeed, the publication of each new paper adds disproportionately to the citations for the already most-cited papers.
Second, if the arrival rate of new ideas is too fast, competition among new ideas may prevent any of the new ideas from becoming known and accepted field wide.
Supposing they are correct, it’s not totally clear what to do. At the biggest level we could fund fewer researchers in large fields, or push more fields to be like economics, where the quality of each researcher’s publications is valued much more than the quantity. But what can an individual researcher do differently? One idea is “boutique science” or “hipster science”, trying to find the smallest or newest field you could reasonably attach yourself to.
Another idea is that the role of generalists and synthesizers is becoming more valuable, as Tyler Cowen often says and David Esptein applies to science in his book Range. When papers are coming out faster than anyone can read, we need more people to sift through them and explain which few are actually important and which are forgettable or wrong. There are lots of ways to do this- review articles, meta-analysis, replication at scale, and of course blogs. But the junk pile is going to keep growing, so we’ll need new and better ways of finding the hidden gems.
Jamal Greene was the most recent guest on Conversations with Tyler. This is how Greene describes his work habits as an academic with children:
GREENE: … my most effective work habit is to use the entire day to work. I get a lot of work done late at night. Most of my time during the day is spent teaching classes or meeting with students, and all writing and reading and preparation and everything is much later. That means I don’t watch television shows. It’s a really extended workday.
I work during soccer practices. I work sitting in the car while my kids are doing something or other. I don’t segregate times of the day where I can’t work.
One thing I find personally is that if I’m doing empirical work then I really need to be inside with at least one external monitor. As much as I like the idea of working from the pool (referencing the viral video of the week) being at my office is the best set up.
2. Currently I am teaching an online asynchronous class. Considering that my students are on the move in different places right now, I decided to create a podcast assignment. This seems to have gone over well. One student had a criticism for the episode that she chose: it was not entertaining. Another student complained that his episode had too much fluff about the personal lives of the speakers. This raises the interesting question of how the experts manage to make podcasts informative without being boring. It’s an art. Talking about your personal life to break up the subject matter can work but it can also feel like a waste of time.
Something that stood out to me was the sheer intensity of these guys. Liberty is a serious topic, but I’ll just share something that is funny from the book.
In the middle of a long fiery speech of Patrick Henry, the book inserts a line in brackets:
What will then become of you and your rights? Will not absolute despotism ensue? {Here Mr. Henry strongly and pathetically expatiated on the probability of the President’s enslaving Americans, and the horrible consequences that must result.}
A footnote explains that stenographer had difficulty keeping up with Mr. Henry and was occasionally reduced to recording a mere summary of his words. It’s impressive that a stenographer could have gotten as much as they did.
I came away from the book thinking that people should talk more about this moment in history, and then I started noticing when people do talk about it. In fact, Tyler was interviewing a constitutional scholar this week and explicitly addressed the idea of “federalism.”
Before I did research, I thought that deaf people simply could not hear. After seeing the Spiderman episodes that featured Daredevil, I believed that it was plausible and likely that deaf people had some sort of cognitive or sensory compensatory skill.
But it wasn’t until recently that I learned of the Deaf Studies field. There is an entire field that’s dedicated to studying deaf people. It’s related to, but not the same as Disability Studies. In fact, there are some sharp divisions between the two fields.
Courtesy of the St. Louis Fed, you can download a report published in 1958 titled “Automation and Employment Opportunities for Office-Workers: A Report on the Effect of Electronic Computers on Employment of Clerical Workers, with a Special Report on Programmers.”
I teach students about data and software to prepare them to enter the hot field of business analytics. It has been a growing field for a few years, especially since the advent of “Big Data”. Something I explain in class is how brand-new technology has changed business.
Reading this report forced me to re-think just how new data analytics is. The authors saw machines in use for data processing and correctly predicted that this would be a dynamic source of new jobs.
The introduction states that millions of “clerical workers” were employed in the United States. That fact would have been obvious at the time, but today we might not realize just how many humans would be needed to store and fetch the files we access regularly on our computers. The creation of clerical jobs was especially important for women.
In view of the volume of work that needed to be done, installing new computers was economical. “A computer system can automatically do such jobs as prepare payrolls for thousands of employees, control inventory on a multitude of items…”
“Although computers are often described as machines that can “think,” that is, of course, not so. Like other machines, they must be operated or controlled by people… The people who prepare the instructions are called programmers.”
“Electronic computers were developed during World War II as an aid in solving intricate scientific and engineering problems such as gunfire control, but their application to the processing of office data is more recent. The Federal Government lead the way in 1951, when an electronic computer was installed by the Bureau of the Census…”
The authors see the primary role of computers in business as a way to automate the routine work that could be performed by clerks. Secondly, they state that computers can by used for solving complex math problems “such as those related to launching and tracking earth satellites.”
The report was created for young people who are considering their own choices for education and careers. The authors describe the programming but also various machine support roles. For example, the Coding Clerk’s job is to convert the programmers’ instructions into “machine language”.
The authors recognize that computers will replace some of the traditional clerk roles. “These developments will not only increase the output of clerical workers and slow down growth in clerical employment, but will also change the character of many jobs… Many of the new jobs … will generally pay better and require higher levels of skill and training than most other clerical jobs.” The next sentence is where the authors fail to predict PCs and the internet: “Moreover, a continued increase is expected in the number of officeworkers in jobs not greatly affected by office automation – for example, secretary, stenographer, messenger, receptionist, and others involving contacts with customers and the public.”
The discussion of women in the workplace is clinical in tone. Turnover is high in the clerical fields because many young women stop working when they get married or have children.
There is a special report on “programmers”, one of the newest occupations in the country. Programmers specialize in either of the following: 1) “processing the great masses of data which have to be handled in large business and government offices” 2) “solving scientific and engineering problems”.
The authors describe typical training and career paths. At the time, a college student could not major in computer science. Companies were filling most positions by selecting employees familiar with the subject matter and giving them training in programming. A few colleges purchased computers and provided some training opportunities.
The culture was different back then. “Although many employers recognize the ability of women to do programming, they are reluctant to pay for their training in view of the large proportion of women who stop working…” The authors tip off their female readers that they are more likely to get training in government than industry, if they aspire to be programmers in the 1950’s. Today, the risk and cost of training has largely shifted from the employer to the worker. If you are interested in the topic of bootcamps and STEM pipelines, read the document for their discussion of education.
These authors made a good long-term prediction because they anticipated the business analytics boom. “Continued expansion in employment of programmers is expected over the long run… In offices where the volume of recordkeeping is great, there will continue to be need to reduce the cost of processing tremendous amounts of data and to produce more timely reports on which management decision can be based.” After explaining salary, they talk about perks: “Programmers usually work in well lighted, air-conditioned, modern offices. Employers make special efforts to provide better than average surroundings for programmers, so that they may concentrate to achieve the extreme accuracy necessary for programming.” The nap pods of Silicon Valley have a long history that can be traced back to the Census Bureau.
On the Bretton Goods podcast, host Pradyumna Prasad asked student Trevor Chow about blogs. To start the segment, Prasad noted that there has been an increase in what he called “econ blogs” in the past 2-3 years. Will that trend continue? Prasad believes that this is not sustainable because: 1) he thinks the paid subscriber model will not support many writers, which leads to 2) bloggers writing for free will run out of time and energy.
Chow replied that he thinks the recent explosion is partly due to Substack, which makes it easy to start blogging. Chow described the current climate as a “flourishing blogosphere.” He assumes that some people started during a Covid shutdown when the opportunity cost was low. Some of the younger people might shift their focus, as he did when his interests changed, but he believes that many of the blogs started in this phase are here to stay. Both young men think about longevity.
Prasad asked, “What are the qualities of the most successful bloggers across time?”
Chow replied that the only blog that has influenced him “across time” is Marginal Revolution, partly because few writers stick with blogging. Chow thinks a successful blogger over time would find a special niche. I have a similar intuition, even though MR is not about a niche topic. If everyone is checking MR for their “daily links”, then it’s unlikely that inferior new aggregator blogs will attract large numbers of readers. Also, Twitter largely fills that role now.
The fact that duration was discussed more than quality is interesting. To blog is to enter a network and join a community. Part of sticking around for a while is not just writing but also reading and paying attention to the work of others. Good writing is a necessary but not sufficient component of what would be considered a successful blog.
As an economist, I was happy to hear Prasad open this segment by talking about “econ blogs”. Econ blogging occurs when people are interesting online, even if the topic is outside of the traditional domain of economists. I think this is partly due to Tyler Cowen both being prolific and also willing to engage non-standard thinkers.
I enjoyed the podcast. It raised some questions which I posed to Tyler Cowen, the OG econ blogger. We all know that MR generates a high level of engagement, today. My first question was:
1. What was the evolution of reader engagement with MR? How long did you work before a lot of people were reading, commenting?
Cowen: It took us 3-4 years to have a lot of readers. but I never tracked the numbers very closely. When I started, I was thrilled by the notion of 5,000 readers a day — of course we have done many times more than that.
2. The consensus is that many new people have started since 2020, which I believe is something that you called for. Do you now see the space as, in some sense, saturated, or would you encourage more people to keep joining now?
Cowen: I don’t think it is saturated now.
3. For bloggers who started since 2020, should they quit if the opportunity cost increases?
Cowen: The main thing is simply whether you enjoy it and learn from it! If so, reason to continue. That sounds trivial, but it is really the bottom line.
Should the new bloggers keep going? Yes, if you enjoy it and learn from it. Is it too late to start? No, if you will enjoy blogging and learn from it.
The blog form is better than a 280-character tweet for capturing nuance. Something I learn from blogging, which might not be obvious from the outside, is that I have some bad ideas. Sometimes trying to write out a piece teaches me that I had an unsupported thought. It would be good if more people would stop scrolling for an hour a week and try to write out an argument.
Co-blogger Mike alerted me to this comic:
This summary by @ZachWeiner of "The University" as an institution is so darkly and romantically pitch perfect I stopped breathing for a few seconds. https://t.co/QbSAv2lTV3
The comic first describes a cynical take on academia, with which I don’t fully agree. Then, the woman paints a picture of an alternative haven for intellectual conversation. Can econ blogs be an old pub where the people are always and only there in earnest? “Most people don’t even want to go in, and you certainly don’t get credentials for descending the stairs.”
Economist Writing Every Day 