Author: Joy Buchanan

What we pay for the thing that some workers do that most people do not

Joy Buchanan2 Comments

In middle school, I broke my leg in a soccer tournament game. I needed to go to the hospital and get extra support for the next month. Some of the workers who helped me were not highly paid, but my value of their services was very high.

Why bring this up? There has been conversation about the label “low skill” work this week. Brian Albrecht summarized the debate. Brian tangentially mentioned the “diamond-water paradox,” but I think it is worth talking more about that. Economists have a few models and stories that change the way you think about the world.

When I teach Labor Economics, we read an excerpt from Average is Over and then I explain the diamond-water paradox in class. I ask the students why diamonds cost more than water, even though water is more important. The answer can help us understand how wages get set for human workers (I say “human” because by that time we are deep in the topic of robot workers as substitutes).

I tell my students that some of the low-pay work performed by humans is extremely important. I’m still looking for the perfect illustration here. The one I use goes something like this, which is related to my broken leg anecdote… imagine if you tripped on train tracks and couldn’t get yourself out of the way of an oncoming train. How much would you pay a human to haul you to safety? Almost any human could perform the task. That service would be as valuable as a glass of water if you are about to die from thirst, which is to say that your value for it is almost infinite.

The key to understanding the market price of cleaners as opposed to the high wages for repairing Facebook code is marginal thinking. There is a lot of water, so the next glass is going to be cheap.

In writing Average is Over, Tyler Cowen is trying to understand why wages for the-less-highly-paid-skills have stagnated recently, while wages for the-highly-paid-skills are increasing along with GDP. He brings computers and technology into the conversation, as one culprit for recent changes. There is a limited supply of humans who can show up to a tech job and contribute reliably. “Programmers” are not the only highly paid class of workers, but it’s easy to see that the supply of people who are proficient with Python is limited.

I see two opposing forces in the tech world, which I have been following for a few years. First, we have boot camps, code clubs and all kinds of resources to both equip and encourage people to go into tech. I volunteer to advise a club that provides resources for female college students taking a technical route. On the other hand, lots of people who do get a foot into the door of a tech company become upset and quit.

Here is a quitter (a twitter quitter?):

https://twitter.com/krotondo/status/1427434279408062467

You can read about this specific situation at this woman’s website. It seems like she made the right choice for herself. She is actually on a mission to change tech for women. I’ll reproduce the text here, in case someone can’t see the tweet: “first day at my new job! i am now a ceramicist because it lets me have no commute, make my own hours, decide the value of my work, and bring people joy. make no mistake, i wanted to code, but tech fulfilled none of that. so i hand off the baton. please fix tech while i make pots!”

The point is that she is one of many people who have dropped out of the tech workforce. Those employees who remain are pushed up toward the “diamond market price” and away from the “water market price”. Here is a blog about “burnout” survey data from 2018.

Populations in rich countries are not growing and labor force participation is down. Could the market wage for lower-skill-requirement jobs in the US rise dramatically in the next century, or at least keep pace with the wage increases that were recently enjoyed by those-with-the-capabilities-that-are-highly-valued? Marginal utility still apply, but prices will change if supply shifts.

See my old blog about Andrew Weaver who is researching skills that are in demand.

Remittances Eye-tracking Experiment: Meet the authors and paper

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I am pleased to have been asked to discuss a paper in an ASHE (American Society of Hispanic Economists) session at the 2022 AEA meeting. Our session is “Hispanics and Finance” on Sunday January 9 at 12:15pm Eastern Time.

The paper is “Neuroeconomics for Development: Eye-Tracking to Understand Migrant Remittances”. Here is a bit about each author. Meeting in person is a benefit that I miss this time, since the meeting is virtual.

Eduardo Nakasone of Michigan State University has several papers on information and communication technologies and agricultural markets. I pondered this sentence from one of his abstracts, “Under certain situations, ICTs can improve rural households’ agricultural production, farm profitability, job opportunities, adoption of healthier practices, and risk management. All these effects have the potential to increase wellbeing and food security in rural areas of developing countries. Several challenges to effectively scaling up the use of ICTs for development remain, however.” His prior work on ICTs is relevant to the paper at hand, which is about how migrants utilize information about remittance tools.

Máximo Torero is the Chief Economist of the Food and Agriculture Organization (FAO). He has worked on development and poverty in many capacities including at the World Bank.

Angelino Viceisza, an associate professor at Spelman College, is doing interesting work at the intersection of Development and Experimental Economics. Here is his 2022 paper (Happy New Year!) published in the Journal of Development Economics.  

I am discussing their paper on how migrants choose financial services. The pre-analysis plan is public. Remittance sending is important for migrants and for the entire world economy. The authors remind us that a significant chunk of what migrants earn is “lost” to service fees. The authors are examining how migrants incorporate new information about competitive alternative services.

Some neat aspects of their work:

  • Their subject pool is migrants who send remittances, recruited in the DC area.
  • Like most experiments I am used to, the stakes are real and significant.
  • Not only can they observe which service is selected, but by using eye-tracking they can get a sense of what information was salient or persuasive.

It is potentially a big deal for migrants to compare services more rigorously and switch providers more readily. The internet, as least in theory, makes it easy to find information on transaction fees. Policy makers have even proposed subsidizing websites that compare the fees of money transfer operators (MTOs). The authors are trying to understand how such a website might impact behavior. A basic question is: does information in this format affect behavior? A small change in behavior could have a huge impact on the world economy and recipient countries. Imagine if a country currently receiving a billion dollars in remittances had 1% more next year because migrants switched to a more efficient service. Might it be cheaper to nudge people toward low-fee services than to send foreign aid?

Their experiment will reveal whether people make switches based on new information, and it also helps us start to understand which attributes of MTOs migrants consider. Their design includes a treatment manipulation that sometimes emphasizes either transfer speed or user reviews.

If you have read this far hoping for a summary of their results, I will disappoint. Their paper is not public yet and data is still being analyzed. I can say that migrant subjects do sometimes switch their choice of MTO, based on information, in some circumstances. They are more likely to make a switch when the induced stakes are higher. If you tune into the session tomorrow, you will get to hear a summary of preliminary results by the author (not free to public, requires conference registration).

Fiction for Christmas

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I hacked Christmas this year to get two books I had been hearing about from reviewers and friends: Project Hail Mary and My Struggle by Knausgård. I wrapped the sci-fi one for my husband, because he will like it. I handed the weird one to him and asked him to wrap it for me. I killed many birds with one stone. The people who read econ blogs will appreciate my accomplishment.

Right after Christmas I had a plane trip that provided some reading time for My Struggle. I like it. As a warning to others, I wonder if the reason “everyone” thinks it is so relatable is that the types of people who review books share the author’s burning desire to be a writer.

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Will we repeat the Christmas Covid wave?

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EDIT at 7pm, same day as posting: You know you have good friends when someone quietly emails you and tells you that the news about Omicron just got much worse and you should probably edit your post. I’ve been trying to rationalized why this January will be better than last January. Of course if it were not for Omicron, I would expect very little from holiday gatherings among mostly-vaccinated Americans. However, having known Omicron was looming, I probably shouldn’t have even tried to speculate. Get your booster and be prepared to hunker down in January if the 2-3 week data indicates that infections are turning extra-lethal. </edit>

In keeping with the “dismal science” brand, let’s dwell on the horrible death toll of the January 2021 Covid wave in the US that followed the Christmas holiday. Here comes Christmas (and other winter holidays) again, a major public health event.

https://www.cnbc.com/2021/01/27/us-reports-record-number-of-covid-deaths-in-january.html

This graph I borrowed from CNBC shows how fast deaths spiked up after the winter holidays of 2020. See also https://data.cdc.gov/.

According to Google search auto-complete, the public is more interested in whether there will be another Christmas Prince movie than whether there will be another Christmas Covid death wave.

I think it’s unlikely that we will see a repeat of exactly what happened last year. I’ve been looking online for predictions and mostly I have found articles warning that Omicron will cause a some kind of wave. No one wants to commit to predicting how many people will die, because anyone who tries is sure to be wrong. The consensus is that breakthrough infections are likely but that vaccines protect against extreme illness.

Nearly a million Americans have died from Covid already (Jeremy argues for a million). Some of those deaths, in retrospect, can almost certainly be tied to family travel during the holidays in 2020. The January Covid wave has only happened once, so it’s impossible to predict what will happen this time. Unfortunately we may get an interaction from increased holiday travel plus a novel highly infectious variant.

The Omicron variant is spreading fast, but no one knows if it will be worse than we we are currently dealing with from Delta. It seems like triple-vaxxed people are not at high risk, from preliminary data. That is reassuring to me personally. Thank you South Africa for being fast and sharing data with the world. For communities with low vaccination rates, it seems certain that more deaths will result from fast-traveling Omicron. Yet, from my reading this week, it is hard to know if it’s really much worse than what they are currently experiencing from Delta.

I’m keeping a Twitter thread going of what other people are saying. Caleb Watney points out that we have two things going for us. Widely available vaccines keep people safer from infection and reduces the chance of needing medical treatment. Secondly, we have gotten better at treating the disease. Together, that should mean less deaths in January 2022, as long as people seek treatment quickly and hospital capacity does not become a limiting factor. Omicron could multiply cases so quickly that we can’t apply all our best treatments to everyone. That is the biggest reason to worry.

Even though people will be less cautious about winter holiday travel this year than they were last year, the country has been open for many months now, including the recent Thanksgiving holiday. The vulnerable population this time should be smaller, in terms of the people likely to die from Omicron.

To say that we won’t blindly exactly repeat the biggest mortality event of my lifetime is not “optimism”. It seems like this January will not be as bad as last January for the reason Watney states: better medical tech on hand, most importantly vaccines for prevention.

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Watching Get Back

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I enjoyed watching Get Back, the new documentary about making a Beatles album. Sometimes I skipped over rehearsal scenes. The streaming format allows you to treat Get Back like a coffee table book, if you choose, as opposed to a feature film that you watch all the way through in one sitting.

I know very little about The Beatles, aside from recognizing their hit songs. Here are my impressions after watching most of Get Back.

Paul McCartney is a rock star. His hair could have its own line in the closing credits. When Paul goofs off, he appears to be entertaining his bandmates because he loves playing for any audience. Conversely, John Lennon seems to joke around because he does not take their music seriously. Paul is motivated to make the Beatles excellent. Ringo’s ability to show up and be quiet is almost as important as Paul’s ability to lead.

I’ll put up my tribute. Then I’ll add more casual observations.

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Getting hired by a bot is unsettling

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Samford student Savanah Needham identified an interesting recent WSJ article about the use of AI in hiring. Savanah writes:

In The WSJ, we learn that AI is being used for hiring employees rather than a traditional hiring manager, thus job applicants fear that they must impress a robot instead of relying on human interaction to get their dream job. The writer argues that job applicants deserve to know ahead of time how the algorithm will judge them and ought to receive feedback if they are rejected. Her proposal highlights the uncertainty that job candidates face in the newly AI-augmented hiring world.

We desperately need such a system. AI’s widespread use in hiring far outpaces our collective ability to keep it in check—to understand, verify and oversee it. Is a résumé screener identifying promising candidates, or is it picking up irrelevant, or even discriminatory, patterns from historical data? Is a job seeker participating in a fair competition if he or she is unable to pass an online personality test, despite having other qualifications needed for the job?

Julia Stoyanovich, WSJ

Robots can look at social media postings, linguistic analysis of candidates’ writing samples, and video-based interviews that utilize algorithms to analyze speech content, tone of voice, emotional states, nonverbal behaviors, and temperamental clues (HBR 2019). In just a few quick seconds, AI uses all the data it has on you to jump to conclusions. AI uses tools that claim to measure tone of voice, expressions, and other aspects of a candidate’s personality to help “measure how culturally ‘normal’ a person is.”

You spend a large amount of time proving to employers that you are not like the others, you’re different/better than other candidates…but now we need to try and convince a robot that we are “normal.”  

Researchers predict that face-reading AI can soon discern candidates’ sexual and political orientation as well as “internal states” like mood or emotion with a high degree of accuracy. This can be worrisome if the face reader claims that one is “too emotional” or assigns someone to a certain political party. 

If Tyler is talking about a new variant…

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For some Americans, this Thanksgiving was the first holiday that felt normal in a long time. Being re-united, without Covid restrictions, is something to celebrate.

On the other hand, a new coronavirus variant was just discovered in South Africa. It’s scary enough that travel bans might be imposed. We have all (just about) learned to live with the original strain from Wuhan, but scientists want time to figure out how dangerous and infectious this new strain is. Maybe at this point people are tired of being lectured about risks. No matter how much or little a person sacrificed for Covid-19, they might feel like that storyline has become too boring to deserve any more of our attention. We cannot stop looking out for new variants that might force us to put cherished traditions on hold again. Coronaviruses kill. My advice is to keep following news from Tyler Cowen, Alex Tabarrok, and Emily Oster.

Oster has been consistently reasonable about family and health risks. She argued to open schools and essentially said that you can see grandparents if the risk is small enough (even though the risks are never zero). As I said before, another trustworthy source of information throughout the pandemic has been Tyler and Alex, who put up almost all of their material in real time at Marginal Revolution.

I’ll share something a friend wrote to me today:

Although [his wife’s name]’s chemo treatment continues to show good long-term signs, this morning we discovered that [she] tested positive for COVID. That’s bad news, the good news is that [she] is already getting the antibody treatment and some extra fluids at the hospital as I write this.

“The antibody treatment” did not exist when the first Covid-19 waves swept through New York with such devastating consequences.

If the newest strain turns out to be a serious development, then in many ways we are better prepared to deal with it than we were before. We probably will blow through the red tape on at-home rapid tests faster the next time around (I’m such an optimist!). We already have contact tracing apps that protect privacy. Vaccine scheduling software is already in place. Everyone has masks at home.

The biggest difficulty I foresee is not coming up with scientific solutions but agreeing as a society about which tools to use. Some people might (will) not even believe the new strain is real.

EWED was started right at the moment when Marginal Revolution commentary on Covid seemed the most crucial. So, sometimes I will do little more here than keep up the echo. Do tweets, phone calls, letters, blogs, or talk about Covid around the Thanksgiving table. Don’t give up.

It’s now clear, whether or not the news out of South Africa turns out to be serious, that we are living with a new problem that will last a long time. It’s a marathon, not a sprint.

If you ever read much of the New Testament, you’ll see a theme in the letters of Paul to cities he has visited. The brand-new churches were doing well, while he was with them in person. Then time goes by and the community or doctrine starts to fray.

Paul wrote these words to the church in Galatia, more than a year after he had visited them:

Let us not become weary in doing good, for at the proper time we will reap a harvest if we do not give up. 

Galatians 6:9
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Word Golf is a new online game

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If you like Scrabble or Family Feud, then you might enjoy playing Word Golf. You can get started for free immediately by going to word.golf

You play by thinking of word associations to click from one concept to another. The challenge will get you thinking. Every game only takes about one minute, and there are simple instructions on the website to get you going right away. Unlike chess, you can do this for fun even without any time commitment.

Like Mike and Jeremy before me this week, I am writing about something I saw at the Emergent Ventures conference. It was an inspiring type of event. I met the young creator of Word Golf and was inspired by his vision for a new intellectual sport.

The game is built from data on how often words appear together on the internet. That’s why I compare it to the TV guessing game show Family Feud. You are not just thinking of synonyms to jump from one word to another. The challenge is to think of what words other people typically use in the same online article.

Word Golf is probably a better use of time than Candy Crush or Solitaire. (I played a lot of computer FreeCell at one point in my life but not anymore.)

Data continues to improve sports performance

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Joy: As a Data Analytics teacher, I often think about the applications of machine intelligence to work processes. Samford undergraduate Copeland Petitfils has written the following blog, which is a reminder to me that there are still many potential areas for growth.

Since “Moneyball”, we have seen the growth of analytics throughout sports. However, many teams have stuck to the same old way of playing baseball, like the Braves. This past May, the Braves took a new innovative approach and saw room for growth on their defensive side.

The general manager, Alex Anthopoulos, implemented a radical strategy and improved the defense by using shifts with data analytics. While “Moneyball” looked at the statistics of acquiring cheaper players who had good batting averages and improved the offensive side, the Braves looked at improving the defensive side and the way they shift between pitches to improve their chances of getting a ground ball out. A defensive shift in baseball refers to the infield changing positions from normal to a certain area of the infield based on the pitches and using stat cast to predict where the batter is most likely to hit the ball depending on the type of pitches. Shifting can increase the probability for players to get ground balls out rather than hits.

Statistically, the Braves ranked at the bottom of defensive shifts in the MLB, and Anthopoulos, the general manager, saw this as an opportunity to improve. The Braves started the 2021 season with no shifting at all to shifting on 50.6% of pitches by the end of the year, which was the highest in baseball this year only behind the Dodgers. The shifting ultimately allows the Braves to improve in converting ground balls to out rather than turning into hits. At the start of the season, the Braves converted under 75% of ground balls into outs which ranked middle of the pack in defense. However, since implementing the shift the number jumped to 77%, which was the second-best in baseball. Although these jumps in percentages seem small, they allowed the Braves to field 25 more ground balls into outs rather than hits.

The data analytics the Braves used allowed the players to be put in a better position to succeed, and as the season progressed, they started to get better and better at it. These decisions turned around the Braves’ season, and now they are on their way to the World Series for the first time since 1999 after beating the Dodgers in the NL Championship.

Coda by Joy: That said, guess who failed at data driven decision making? Zillow!

In a statement Tuesday, Chief Executive Rich Barton said Zillow had failed to predict the pace of home-price appreciation accurately, marking an end to a venture the company once said could generate $20 billion a year. Instead, the company said it now plans to cut 25% of its workforce… “We’ve determined the unpredictability in forecasting home prices far exceeds what we anticipated and continuing to scale Zillow Offers would result in too much earnings and balance-sheet volatility,” Mr. Barton said.