Productivity

Old Fashioned Function Keys

Zachary Bartsch1 Comment

Your Function Keys Are Cooler Than You Think
by someone who used to press F1 by mistake

Ever notice the F keys on your keyboard? F1 through F12. Sitting at the top like unused shelf space. If you’re at a computer now, take a glance. I used to think they did nothing, or at least nothing for me. Maybe experts used them. Experts who know what BIOS and DOS are.  But for me, just little space fillers with no purpose. I frequently pressed F1 by accident rather than escape. A help window would pop up, wasting half a second of my life until I closed it.

But the Fn keys (function keys) are sneaky useful. They can save you serious time. No clicking. No dragging. No fumbling with touchpad mis-clicks.

When using a web browser, F5 refreshes the web page. Windows has added the same functionality for folders too, updating recently edited files. Fast and easy. F11 changes your web browser view to full screen. Great for long reads or historical documents. F12 shows the guts of a webpage. That’s perfect if you web scrape or need to know what things are called behind the scenes. Ctrl + F4 closes a tab. Alt + F4 shuts the whole application instance down. That last one works for almost all applications.

Excel? F4 saves so much of your life. It toggles absolute cell, row, and column references. Have you ever watched someone try to click on the right spot with their touchpad and manually press the ‘$’ sign… twice? I can feel myself slowly creeping toward death as my life wastes away. Whereas pressing F4 lets you get on with your life. F12 in most Microsoft applications is ‘Save As’. No need to find the floppy disk image on that small laptop screen. PowerPoint has its own tricks—F5 begins the presentation. Shift + F5 starts it from the current slide. Not bad. And don’t forget F7! That’s the spellcheck hotkey. But now it’s been expanded to include grammar, clarity, concision, and inclusivity.

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Join Joy to discuss Artificial Intelligence in May 2025

Joy Buchanan1 Comment

Podcasts are emerging as one of the key mediums for getting expert timely opinions and news about artificial intelligence. For example, EconTalk (Russ Roberts) has featured some of the most famous voices in AI discourse:

EconTalk: Eliezer Yudkowsky on the Dangers of AI (2023)

EconTalk: Marc Andreessen on Why AI Will Save the World 

EconTalk: Reid Hoffman on Why AI Is Good for Humans

If you would like to engage in a discussion about these topics in May, please sign up for the session I am leading. It is free, but you do need to sign up for the Liberty Fund Portal.

The event consists of two weeks when you can do a discussion board style conversation asynchronously with other interested listeners and readers. Lastly, there is a zoom meeting to bring everyone together on May 21. You don’t have to do all three of the parts.

Further description for those who are interested:

Timeless: Artificial Intelligence: Doom or Bloom?

with Joy Buchanan

Time: May 5-9, 2025 and May 12-16, 2025

How will humans succeed (or survive) in the Age of AI? 

Russ Roberts brought the world’s leading thinkers about artificial intelligence to the EconTalk audience and was early to the trend. He hosted Nick Bostrom on Superintelligence in 2014, more than a decade before the world was shocked into thinking harder about AI after meeting ChatGPT. 

We will discuss the future of humanity by revisiting or discovering some of Robert’s best EconTalk podcasts on this topic and reading complementary texts. Participants can join in for part or all of the series. 

Week 1: May 5-9, 2025

An asynchronous discussion, with an emphasis on possible negative outcomes from AI, such as unemployment, social disengagement, and existential risk. Participants will be invited to suggest special topics for a separate session that will be held on Zoom on May 21, 2025, 2:00-3:30 pm EDT. 

Required Readings: EconTalk: Eliezer Yudkowsky on the Dangers of AI (2023)

EconTalk: Erik Hoel on the Threat to Humanity from AI (2023) with an EconTalk Extra Who’s Afraid of Artificial Intelligence? by Joy Buchanan

“Trurl’s Electronic Bard” (1965) by Stanisław Lem. 

In this prescient short story, a scientist builds a poetry-writing machine. Sound familiar? (If anyone participated in the Life and Fate reading club with Russ and Tyler, there are parallels between Lem’s work and Vasily Grossman’s “Life and Fate” (1959), as both emerged from Eastern European intellectual traditions during the Cold War.)

Optional Readings: “Technological Singularity” by Vernor Vinge. Field Robotics Center, Carnegie Mellon U., 1993.

“‘I am Bing, and I Am Evil’: Microsoft’s new AI really does herald a global threat” by Erik Hoel. The Intrinsic Perspective Substack, February 16, 2023.

“Situational Awareness” (2024) by Leopold Aschenbrenner 

Week 2: May 12-16, 2025

An asynchronous discussion, emphasizing the promise of AI as the next technological breakthrough that will make us richer.
Required Readings: EconTalk: Marc Andreessen on Why AI Will Save the World 

EconTalk: Reid Hoffman on Why AI Is Good for Humans

Optional Readings: EconTalk: Tyler Cowen on the Risks and Impact of Artificial Intelligence (2023)

“ChatGPT Hallucinates Nonexistent Citations: Evidence from Economics” (2024) 

Joy Buchanan with Stephen Hill and Olga Shapoval. The American Economist, 69(1), 80-87.

What the Superintelligence can do for us (Joy Buchanan, 2024)

Dwarkesh Podcast “Tyler Cowen – Hayek, Keynes, & Smith on AI, Animal Spirits, Anarchy, & Growth”

Week 3: May 21, 2025, 2:00-3:30 pm EDT (Zoom meeting)
Pre-registration is required, and we ask you to register only if you can be present for the entire session. Readings are available online. We will get to talk in the same zoom room!

Required Readings: Great Antidote podcast with Katherine Mangu-Ward on AI: Reality, Concerns, and Optimism

Additional readings will be added based partially on previous sessions’ participants’ suggestions

Optional Readings: Rediscovering David Hume’s Wisdom in the Age of AI (Joy Buchanan, EconLog, 2024)

“Professor tailored AI tutor to physics course. Engagement doubled” The Harvard Gazette. 2024. 

Please email Joy if you have any trouble signing up for the virtual event.

Now published: Human capital of the US deaf Population, 1850-1910

Zachary BartschLeave a comment

Myself and a student coauthor worked hard on our article that is now published in Social Science History. It’s the first modern statistical analysis of the historical deaf population. We bring an economic lens and statistical treatment to a topic that previously included much anecdotal evidence and case study. We hope that future authors can improve on our work in ways that meet and surpass the quantitative methods that we employed.

Our contributions include:

  • A human capital model of deafness that’s agnostic about its productivity implications and treats deaf individuals as if they made decisions rationally.
  • A better understanding of school attendance rates and the ages at which they attended.
  • Deaf children were much more likely to be neither in school nor employed earlier in US history.
  • The negative impact of state ‘school for the deaf’ availability on subsequent economic outcomes among deaf adults. We speculate that they attended schools due to the social benefits of access to community.
  • Deaf workers did not avoid occupations where their deafness would be incidentally detectable by trade partners, implying that animus discrimination was not systemically important for economic outcomes.
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RGDP Underestimates Welfare

Zachary BartschLeave a comment

Like many Principles of Macroeconomics courses, mine begins with an introduction to GDP. We motivate RGDP as a measure of economic activity and NGDP as an indicator of income or total expenditures. But how does more RGDP imply that we are better off, even materially? One entirely appropriate answer is that the quantities of output are greater. Given some population, greater output means more final goods and services per person. So, our real income increases.  But what else can we say?

First, after adjusting for price changes, we can say that GDP underestimates the value that people place on goods and services that are transacted in markets. Given that 1) demand slopes down and 2) transactions are consensual, it stands to reason that everyone pays no more than their maximum value for things. This implies that people’s willingness to pay for goods surpasses their actual expenditures. Therefore, RGDP is a lower bound to the economic benefits that people enjoy. Without knowing the marginal value that people place on all quantities less than those that they actually buy, we have no idea how much more value is actually provided in our economy.

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No Tech Workers or No Tech Jobs?

Joy Buchanan1 Comment

Several recent tweets(xeets) about tech talent re-ignited the conversation about native-born STEM workers and American policy. For the Very Online, Christmas 2024 was about the H-1B Elon tweets.

Elon Musk implies that “elite” engineering talent cannot be found among Americans. Do Americans need to import talent?

What would it take to home grow elite engineering talent? Some people interpreted this Vivek tweet to mean that American kids need to be shut away into cram schools.

The reason top tech companies often hire foreign-born & first-generation engineers over “native” Americans isn’t because of an innate American IQ deficit (a lazy & wrong explanation). A key part of it comes down to the c-word: culture. Tough questions demand tough answers & if we’re really serious about fixing the problem, we have to confront the TRUTH:

Our American culture has venerated mediocrity over excellence for way too long (at least since the 90s and likely longer). That doesn’t start in college, it starts YOUNG. A culture that celebrates the prom queen over the math olympiad champ, or the jock over the valedictorian, will not produce the best engineers.

– Vivek tweet on Dec. 26, 2024

My (Joy’s) opinion is that American culture could change on the margin to grow better talent (and specifically tech talent) resulting in a more competitive adult labor force. This need not come at the expense of all leisure. College students should spend 10 more hours a week studying, which would still leave time for socializing. Elementary school kids could spend 7 more hours a week reading and still have time for TV or sports.

I’ve said in several places that younger kids should read complex books before the age of 9 instead of placing a heavy focus on STEM skills. Narratives like The Hobbit are perfect for this. Short fables are great for younger kids.  

The flip side of this, which creates the puzzle, is: Why does it feel difficult to get a job in tech? Why do we see headlines like “Laid-off techies face ‘sense of impending doom’ with job cuts at highest since dot-com crash” (2024)

Which is it? Is there a glut of engineering talent in America? Are young men who trained for tech frustrated that employers bring in foreign talent to undercut wages? Is there no talent here? Are H-1B’s a national security necessity to make up the deficit of quantity?

Previously, I wrote an experimental paper called “Willingness to be Paid: Who Trains for Tech Jobs?” to explore what might push college students toward computer programming. To the extent I found evidence that preferences matter, culture could indeed have some impact on the seemingly more impersonal forces of supply and demand.

For a more updated perspective, I asked two friends with domain-specific knowledge in American tech hiring for comments. I appreciate their rapid responses. My slowness, not theirs, explains this post coming out weeks after the discourse has moved on. Note that there are differences between the “engineers” whom Elon has in mind in the tweet below versus the broader software engineering world.

Software Engineer John Vandivier responds:

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Excel’s Weird (In)Convenience: COUNTIF, AVERAGEIF, & STDEVIF

Zachary Bartsch1 Comment

Excel is an attractive tool for those who consider themselves ‘not a math person’.  In particular, it visually organizes information and has many built-in functions that can make your life easier. You can use math if you want, but there are functions that can help even the non-math folks

If you are a moderate Excel user, then you likely already know about the AVERAGE and COUNT functions. If you’re a little but statistically inclined, then you might also know about the STDEV.S function (STDEV is deprecated). All of these functions are super easy and only have one argument. You just enter the cells (array) that you want to describe, and you’re done. Below is an example with the ‘code’ for convenience.

=COUNT(A2:A21)
=AVERAGE(A2:A21)
=STDEV.S(A2:A21)

If you do some slightly more sophisticated data analysis, then you may know about the “IF” function. It’s relatively simple; if a proposition is true (such as a cell value condition), then it returns a value. If the proposition is false, then it returns another value. You can even create nested “IF”s in which a condition being satisfied results in another tested proposition. Back when excel had more limited functions, we had to think creatively because there was a limit to the number of nested “IF” functions that were permitted in a single cell. Prior to 2007, a maximum of seven “IF” functions were permitted. Now the maximum is 64 nested “IF”s. If you’re using that many “IF”s, then you might have bigger problems than the “IF” limitations.

Another improvement that Excel introduced in 2019 was easier array arguments. In prior versions of Excel, there was some mild complication in how array functions must be entered (curly brackets: {}). But now, Excel is usually smart enough to handle the arrays without special instructions.  Subsequently, Excel has introduced functions that combine the array features with the “IF” functions to save people keystrokes and brainpower.

Looking at the example data we see that there is an identifier that marks the values as “A” or “B”. Say that you want to describe these subgroups. Historically, if you weren’t already a sophisticated user, then you’d need to sort the data and then calculate the functions for each subgroup’s array. That’s no big deal for small sets of data and two possible ID values, but it’s a more time-consuming task for many possible ID values and multiple ID categories.

The early “IF” statements allowed users to analyze certain values of the data, such as those that were greater than, less than, or equal to a particular value. But, what if you want to describe the data according to criteria in another column (such as ID)? That’s where Excel has some more sophisticated functions for convenience. However, as a general matter of user interface, it will be clear why these are somewhat… awkward.

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More Productive than “Smart”

Zachary BartschLeave a comment

Public choice economists emphasize the process by which we select political leaders. Electoral and voting rules influence the type of leaders we get. Institutional economists agree and go one step further. Who we choose matters less than the environment we place them in. Leaders, regardless of their personal qualities, respond to the incentives that surround them. The ultimate policies, therefore, largely conform to those incentives. From this perspective, it’s important to adopt institutional incentives for leaders to promote policies oriented toward economic growth and provide the option to flourish.

The same principle applies to the private economy. Productivity is crucial, and higher IQ often correlates with greater productivity. Yet, genetic endowment—including IQ—is beyond individual control. Many other determinants of productivity are not exogenous when we can affect policy. Let’s adopt policies that allow individuals with lower IQ to act productively as if they had higher IQ. Protecting the freedom to contract and private property rights creates conditions whereby even those at the lower end of the cognitive ability distribution can thrive. These principles expand their opportunities. Market signals give them valuable feedback on their activities and enable them to contribute to the economy.

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Human Capital is Technologically Contingent

Zachary BartschLeave a comment

The seminal paper in the theory of human capital by Paul Romer. In it, he recognizes different types of human capital such as physical skills, educational skills, work experience, etc. Subsequent macro papers in the literature often just clumped together some measures of human capital as if it was a single substance. There were a lot of cross-country RGDP per capita comparison papers that included determinants like ‘years of schooling’, ‘IQ’, and the like.

But more recent papers have been more detailed. For example, the average biological difference between men and women concerning brawn has been shown to be a determinant of occupational choice. If we believe that comparative advantage is true, then occupational sorting by human capital is the theoretical outcome. That’s exactly what we see in the data.

Similarly, my own forthcoming paper on the 19th century US deaf population illustrates that people who had less sensitive or absent ability to hear engaged in fewer management and commercial occupations, or were less commonly in industries that required strong verbal skills (on average).

Clearly, there are different types of human capital and they matter differently for different jobs. Technology also changes what skills are necessary to boot. This post shares some thoughts about how to think about human capital and technology. The easiest way to illustrate the points is with a simplified example.

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Persistent Beliefs

Zachary BartschLeave a comment

The things that happen between people’s ears are difficult to study. Similarly, the actions that we take and the symbolic gestures that we communicate to the people around us are also difficult to study. We often and easily perceive the social signals of otherwise mundane activities, but they are nearly impossible to quantify systematically beyond 1st person accounts. And that’s me being generous. Part of the reason that these things are hard to study is that communication requires both a transmitter and a receiver. One person transmits a message and another person receives it. Sometimes, they’re on slightly or very different wavelengths and the message gets garbled or sent inadvertently and then conflict ensues.

Having common beliefs and understandings about the world help us to communicate more effectively. Those beliefs also tend to be relevant about the material world too. A small example is sunscreen. Because a parent rightly believes that sunscreen will protect their child from short-run pain and long-run sickness, they might lather it on. But, due to their belief, they also signal their love, compassion, and stewardship for their child. A spouse or another adult failing to apply sunscreen to a child signals the lack thereof and conflict can ensue even when the long-term impact of one-time and brief sun exposure is almost zero.

People cry both sad and happy tears because of how they interpret the actions of others – often apart from the other external effects. Therefore, beliefs imbue with costs and benefits even the behaviors that have seemingly immaterial consequences otherwise. We can argue all day about beliefs. And while beliefs might change with temporary changes in the technology, society, and the environment, core beliefs need to be durable over time. Therefore, if this economist were to recommend beliefs, then I would focus on the prerequisite of persistence before even trying to find a locally optimal set.

Here are three inexhaustive criteria for a durable beliefs:

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Services, and Goods, and Software (Oh My!)

Zachary BartschLeave a comment

When I was in high school I remember talking about video game consumption. Yes, an Xbox was more than two hundred dollars, but one could enjoy the next hour of that video game play at a cost of almost zero. Video games lowered the marginal cost and increased the marginal utility of what is measured as leisure. Similarly, the 20th century was the time of mass production. Labor-saving devices and a deluge of goods pervaded. Remember servants? That’s a pre-20th century technology. Domestic work in another person’s house was very popular in the 1800s. Less so as the 20th century progressed. Now we devices that save on both labor and physical resources. Software helps us surpass the historical limits of moving physical objects in the real world.


There’s something that I think about a lot and I’ve been thinking about it for 20 years. It’s simple and not comprehensive, but I still think that it makes sense.

  • Labor is highly regulated and costly.
  • Physical capital is less regulated than labor.
  • Software and writing more generally is less regulated than physical capital.


I think that just about anyone would agree with the above. Labor is regulated by health and safety standards, “human resource” concerns, legal compliance and preemption, environmental impact, and transportation infrastructure, etc. It’s expensive to employ someone, and it’s especially expensive to have them employ their physical labor.

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