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.”
Sometimes, large entities have enough money to throw at a problem that by sheer magnitude they produce something great (albeit at too high a cost). The iPhone app from the FRED is not that thing. But the Excel add-in is something that every macroeconomics professor should consider adding to their toolkit.
Personally, I include links to FRED content in the lecture notes that I provide to students. But FRED makes it easy to do so much more. They now have an add-in that makes accessing data *much* faster. With it, professors can demonstrate in excel their transformations that students can easily replicate. The advantage is that students can learn to access and transform their own data rather than relying on links that I provide them.
The tool is easy enough to find – FRED wants you to use it. After that, the installation is largely automatic.
Installed in excel you will see the below new ribbon option. It’s very user friendly.
By the time most students exit undergrad, they get acquainted with the Aggregate Supply – Aggregate Demand model. I think that this model is so important that my Principles of Macro class spends twice the amount of time on it as on any other topic. The model is nice because it uses the familiar tools of Supply & Demand and throws a macro twist on them. Below is a graph of the short-run AS-AD model.
Quick primer: The AD curve increases to the right and decreases to the left. The Federal Reserve and Federal government can both affect AD by increasing or decreasing total spending in the economy. Economists differ on the circumstances in which one authority is more relevant than another.
The AS curve reflects inflation expectations, short-run productivity (intercept), and nominal rigidity (slope). If inflation expectations rise, then the AS curve shifts up vertically. If there is transitory decline in productivity, then it shifts up vertically and left horizontally.
Nominal rigidity refers to the total spending elasticity of the quantity produced. In laymen’s terms, nominal rigidity describes how production changes when there is a short-run increase in total spending. The figure above displays 3 possible SR-AS’s. AS0 reflects that firms will simply produce more when there is greater spending and they will not raise their prices. AS2 reflects that producers mostly raise prices and increase output only somewhat. AS1 is an intermediate case. One of the things that determines nominal rigidity is how accurate the inflation expectations are. The more accurate the inflation expectations, the more vertical the SR-AS curve appears.*
The AS-AD model has many of the typical S&D features. The initial equilibrium is the intersection between the original AS and AD curves. There is a price and quantity implication when one of the curves move. An increase in AD results in some combination of higher prices and greater output – depending on nominal rigidities. An increase in the SR-AS curve results in some combination of lower prices and higher output – depending on the slope of aggregate demand.
Of course, the real world is complicated – sometimes multiple shocks occur and multiple curves move simultaneously. If that is the case, then we can simply say which curve ‘moved more’. We should also expect that the long-run productive capacity of the economy increased over the past two years, say due to technological improvements, such that the new equilibrium output is several percentage points to the right. We can’t observe the AD and AS curves directly, but we can observe their results.
The big questions are:
What happened during and after the 2020 recession?
To get anywhere new, you need to step off the treadmill
Before tenure, most academics need to publish their work in peer-reviewed journals if they want to keep their jobs. After tenure, most can publish their work anywhere or nowhere and still keep their jobs. This is a dramatic change in incentives, and you’d think it would lead to dramatic changes in behavior, particularly in a field like economics that studies incentives. In some ways it does- most professors spend less time on research after tenure. But if they do keep doing research, it is generally the same kind they did before- it seems surprisingly rare for economists to change what kind of research they do in response to their changed incentives.
On Monday the President of Providence College told me I’ve been promoted to tenured Associate Professor. I spent much of the last 10 years focused on publishing the 26 academic articles that got me here. So now I’m wondering, what do I change when freed from constraints? I’m planning a pivot toward higher-risk, longer time-horizon, potentially higher-reward research:
Different venues– publish things where people will read them, not where its most prestigious. More white papers, working papers, open access. More blog posts and popular articles, more books– not everything needs to be a peer-reviewed academic paper.
Different topics and methods– focus on work that might have policy impact even if it doesn’t publish well. Do more replications, forecasting and related work that moves us toward being a real science that establishes real truths, even if it doesn’t publish well and might anger some people. Make a point of posting data and code publicly so that its easy for others to use.
New skills– develop generalist skills or a 2nd specialty, ideally in a young/developing field like metascience or superforecasting. Breakthroughs are more likely to come that way, especially for someone not at the top of their 1st field. Create slack so that when big opportunities or needs arise, I’m not “too busy” working on old articles to do anything about it (like I was with Covid in February 2020, Bitcoin mining in 2011, et c). Of course, many of the directions I’m considering (prediction markets, consulting, angel investing, hanging around the state house) might never be “research” even if they do pan out.
The GMU economists are good role models here, though they are such outliers now that people don’t realize they often started their careers focused on publishing journal articles (admittedly some weird ones). For instance, Bryan Caplan’s first book came out 4 years after he got tenure. I’d like to hear more examples of people whose research changed for the better after tenure if you have them. I’d also like to hear about the projects you wish someone not concerned about career risk would take on.
I’m happy to be an Associate Professor at Providence College. While I wouldn’t mind hitting some higher rungs of the academic career/prestige ladder (full professor, endowed chair, NBER invitations, et c), I don’t view these as incentives strong enough to distort my choices the way needing to get a job and get tenure did. Now the goal is simply to do the best work I’m capable of, as I see it. As you can tell I’m pulled in a lot of different directions about what this will look like, but I hope that within 5 years it will be clear I’m doing quality work beyond standard applied microeconomics I’ve been exclusively focused on till now. If not, you’ll have this post to hold over me.
“I consider the “wasting of tenure” to be one of the aesthetic crimes one can commit with a wealthy life, and yet I see it all the time” –Tyler Cowen
Last week I went to Disney World for the first time. The decorations live up to the hype. The whole enterprise down to the efficient parking systems was impressive.
Galaxy’s Edge is a new Star Wars themed area in Hollywood Studios
In his book The Decadent Society, Ross Douthat argues that following the Apollo mission, Americans underwent a period of economic stagnation, demographic decline, and intellectual and cultural repetition. I think he makes good points, and every American should grapple with his proposition.
He specifically mentions Disneyland on page 36-37:
But has anything that fits this description happened since the moon mission? … There has been a growth in what [David] Nye calls “the consumer’s sublime” of Disneyland and Las Vegas. … But the hyperloop is a blueprint, Las Vegas is a simulacrum…
Has Douthat been to Orlando recently? Walt Disney was not complacent, and neither are the Disney employees who continue to carry out his vision. Orlando is a place where Americans have built stuff in the past few decades instead of trying to veto all progress.
Perhaps it is a decadent society that overvalues the Disney World pilgrimage. My parents never took me, so I am proof that you can have a good childhood without it. However, to build this zone and enjoy it seems like a perfectly legitimate peacetime activity for a country. People desire to stroll down a safe, beautiful, clean, walkable street with their families. The problem is that so many Americans can only do that for a few days per decade and empty their savings to Disney for the privilege.
There is a pernicious idea that respectable Americans live in towns that look just like 1950 and they do tourism at sites that look like 1850. Walt Disney obviously did not think that way. On Twitter, @EliDourado and @mnolangray are agitating every day to build more better stuff. We don’t need Donald Duck on every corner, but we could create cities that serve families better.
One surprise I found inside of the Tomorrowland zone of Magic Kingdom is an old ride called The Carousel of Progress.
The AEA Data Editor kicked it all off with this tweet:
Please stop using "cd" (in Stata) or "setwd()" (in R) all over the place. Once (maybe, not really), that's enough. Can we mark those commands as "deprecated"?
“Please stop using “cd” (in Stata) or “setwd()” (in R) all over the place. Once (maybe, not really), that’s enough.”
Replies proliferated on #EconTwitter this week. In this blog post I am collecting solutions for R. These days you might share the code used to generate your results for an empirical paper. That code would ideally be easy for other people to run on their own computers. File paths are hard (as I blogged previously).
A project for a single paper might have multiple code files. The code interacts with data stored somewhere. Part of the task of the code is to point the statistical program to the data set. It is frustrating if an outsider is trying to replicate a result and must alter the code in multiple places to point to their own location of the data.
Here is a concise summary of good practice, for any code language: “cd and setwd() specify the directory. When you share code and run on a different computer, they don’t work. Therefore, good practice to only specify once, at the beginning”
It is hard to know when oneself does not have enough context to appreciate a piece of art. When someone else lacks context, it is easier to see.
Consider my children watching last week’s Super Bowl halftime show. Snoop Dogg was performing on a stylized urban-themed stage. My kids could see the same thing I could see. They did not, remotely, “get it”.
You better lose yourself in the music…
“Lose Yourself” performed by Eminem
Children have little context for anything. But this half time show was a cultural moment. Millennials and Gen X experienced an awakening, or perhaps a collective crisis.
The following tweet got 60 thousand likes about how old the performers are. Everyone was calculating how many years had gone by since this hip hop and rap music was new. Decades have passed and now we who have the context to understand this music are feeling old.
For everyone googling:
Dr Dre: 56 years old
Snoop Dogg: 50 years old
50 Cent: 46 years old
— Washington Post TikTok Guy 🤹🏼♂️ (@davejorgenson) February 14, 2022
In some nursing home in the year 2070, school children will trudge in and sing “Lose Yourself” because it makes the old people happy. The kids will not understand the appeal.
For all the people sweating about Dre being old and now facing their own mortality, lemme point out that plenty of y’all don’t even know who Kendrick Lamar is. There was a more obvious sign of your oldness staring at you the whole time.
Matt and Ben are tweeting about music in code, but it happens to be a code I know. There are many codes that I do not know. I think that is part of what Tyler means in his latest posts about how “context is scarce”.
The real reason for writing about context this week is not the Super Bowl. I was reading yet more articles about tech skills and labor demand.* Once again, I came across the issue of soft skills. Could we say, “Soft skills are that which is scarce”?
When workers lack hard skills, it seems straight-forward to pack them off to a bootcamp. Teach them, for example, some functions in a programming language. The solution to a lack of soft skills is less clear, although maybe that is what decades of modern education is for. Corporate workers today need to know when to apply their skills and what tone to use in their email communication. They must not embarrass the company.
If a manager tells a worker to do “X task,” they cannot explain every detail. The worker needs to have the context to carry out the work on their own. Workers need to know the code.**
Could that be why so many employers desire a bachelor’s degree? Tyler wrote:
9. So much of education is teaching people context. That is why it is hard, and also why it often does not seem like real learning.
Does this explain why there is simultaneously age discrimination and the ubiquitous “5 years of experience” hurdle for good jobs? Managers are looking for the sweet spot of current technical savvy and institutional context.
* I was reading a report by Quinn Burke. Here’s a published paper on soft skills and STEM. Here’s a blog of mine in which I wrote, “Trust falls and Tolkien is the prescription for this workforce.”
Brian Oldreive is a Zimbabwean, born there in 1943. A star cricket player as a young man, he moved on to become a successful tobacco farmer. In 1978, he became convinced (given the harm that tobacco causes) that he should no longer grow tobacco. When he tried to switch to food crops like corn (called maize in Africa), using standard agricultural practices, he could not make a go of it. He ended up losing his farm and his livelihood due to his moral stand against growing tobacco. He went to work for another large farm, but even there it was a struggle to grow food at a profit. Soil was eroding and crop yields were falling.
He began to think that maybe there was a better way to farm than the usual Western model. One day in 1984 when he was walking in the forest, he noticed that the trees and bushes there grew just fine, with no help from humans, no plowing or irrigation. How was that possible? He observed two things. First, the ground was covered with a thick layer of leaves and other debris, which formed a natural mulch. Beneath this mulch layer (“God’s blanket”), the soil was moist. This was while the region was experiencing drought, and regular farmers’ fields were parched. Secondly, the undisturbed mulch layer naturally decayed to return nutrients to the soil.
Oldreive parlayed those observations into a system of no-till agriculture which mimics the created order. He called this “Farming God’s Way”. The emphasis is on high productivity from a small plot. This involves precision planting at the proper time, crop rotation (corn/beans), and deep mulching to retain moisture and keep weeds down. Nutrients are supplied by both compost and chemical fertilizers.
This method can be practiced by farmers owning no tool other than a hoe. This breaks the cycle of farmers or nations going into debt to purchase expensive Western agricultural machinery, which then may become useless due to inadequate maintenance out in the bush.
This approach contrasts with conventional farming practice which plows up the soil, leaving it to erode away when it rains and to dry out when it doesn’t rain. Plowing also disturbs the natural ordering of the microbial communities within the upper and lower soil layers. (There is aerobic metabolism near the surface, and a whole different anaerobic community in the soil lower down).
Oldreive started by planting one small plot using this approach in the estate he was then managing:
I decided to copy what God does in natural creation and I observed that the leaves fall down on the ground and the grass dies down and there is a protective blanket over the earth, and that is how God preserves soil to infiltrate the water that we receive…
Many people did not believe me and said I was wasting time. But I was not deterred because I was convinced that this method would work. I decided to put the model into practice by starting with just two hectares. I prayed for wisdom and God showed me how to plant maize into wheat straw residue. This is just the same as what God does in nature.
That two-hectare (about 5 acre) plot confounded the skeptics, yielding about ten times more corn per hectare than the local average yields. He then planted more acreage using this approach. Over the next few years, while a number of conventionally-run farms around him went broke, he kept expanding and growing more food with his system.
Oldreive believed these insights were gifts from God which were meant to be shared with others. Therefore, he shifted his effort towards teaching other Africans how to farm with this method.
Economist Writing Every Day 