For most Americans, the Super Bowl is this Sunday. But for several of us at Planet Money, and several thousand economists from all around the world, our Super Bowl was a few weeks ago in Philadelphia … at the annual gathering of the American Economic Association.
This conference is always a who’s who of the econ world. Big-name economists debated on stage some of the most pressing questions in the field: Is there economic opportunity in the U.S. anymore? (Kind of.) Can we still trust government statistics? (Yes.) Did the pandemic change how inflation works? (Still up for debate.)
The real highlight, though, is when economists present their latest research. Like a lot of academic research, economics research can be slow, tedious, laborious. But the papers we get to see presented at this conference are hot off the presses, or even before the presses. They’re in their early drafts, pre-peer review.
It’s in these paper sessions that we peek into what will be news in economics, what big ideas are coming, and we find new ways to think about the things we’ve been covering at Planet Money.
(And while we’re talking nerdy economic learning…if you like this newsletter, check out the Planet Money book coming out in April. If you pre-order it, you can get a free poster to go with it.)
Anyway: here are some papers that jumped out at us at the AEA conference.
Planet Money staffers and alumna Stacey Vanek Smith have lunch during the AEA conference. Nick Fountain/NPR
First AI slows you down, then it speeds you up
One of the interesting papers we discovered at AEA was titled, “The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s).” It’s by Kristina McElheran, Mu-Jeung Yang, Zachary Kroff and Erik Brynjolfsson, and it gives a glimpse into what actually happens to a firm’s productivity when they start using new technologies like AI. They find that first, productivity goes down. Then, it rises. They call this a “J” curve, after the shape of the letter.
The economists collaborated with the Census to collect and analyze data from 2017-2021 on tens of thousands of manufacturing firms that tried adopting AI-related technologies. Things like: machine vision, speech recognition, automated vehicles, AI-enabled industrial robots, and predictive maintenance (They used that one a lot).
The researchers found that when companies first started using AI, their productivity fell dramatically (like the downward curve of the letter “J”). In some cases, productivity fell by as much as 44%. That decline came from early adjustment costs, like the initial cost of investing in a bunch of new robots, training employees on how to use it, and reconfiguring their operations around it.
However — and this is a big however — that early investment paid off for the firms in their study. Their productivity growth looks like the rest of a “J.” A sharp, upward tick. This was especially true for the early adopters, which tended to be newer companies. Staid, older businesses often had a harder time adjusting.
Kristina McElheran, one of the co-authors, tells us that this may be because bigger, older companies could have more organizational infrastructure to retrofit, so it’s more costly.
“What’s baked into all of this is survival. The main tradeoff we see is, can you weather the adjustment period,” McElhern says. “The adjustment adds to cost. But that’s really what the gains are from.”
She adds: “Maybe it’s the classic higher-risk, higher-reward tradeoff.”
How a little wartime science funding pays off for decades
During World War II, the U.S. government had a mission: to win the war. But to do that, it had to keep its soldiers healthy. In previous wars, disease had killed more soldiers than battleground wounds.
So, the government did something novel. It set up the Committee on Medical Research. In other words: it made a big investment in science.
The CMR was a civilian agency that worked with the military to figure out what scientists should research. Like, how should we prepare our soldiers for extreme temperatures? Then, the Committee would solicit research proposals from universities and private foundations, refer them to other expert committees, eventually provide funding, and help them manufacture their findings when relevant – first for the military, and later for the public.
Its crowning achievement was the mass production of penicillin. Scientists had known about penicillin since 1928, when Alexander Fleming famously discovered it. But before the Second World War, it was hard to study. There was barely enough of the chemical for clinical trials, let alone widespread military or civilian use. So, the CMR worked with what was then a small chemical company named Pfizer to research, develop and eventually scale up production, creating what by the 1950s was a widely available antibiotic.
AFP via Getty Images
Even though the CMR disbanded after the war — it was folded into the National Institutes of Health — the CMR’s model of working with government agencies and with the private sector became a kind of template that the U.S. government used to support scientific R&D for decades to come. The CMR was part of the defense industrial complex that helped create Silicon Valley, defense tech, and decades of biomedical research.
Economists Dan Gross and Bhaven Sampat argue in their paper, “The Therapeutic Consequences of War: World War II and the 20th-Century Expansion of Biomedicine,” that this temporary “shock” of research investment had long-run effects. They find that research that came about through the CMR was significant; that over the following decades, there were more scientific discoveries. Greater pharmaceutical innovation. It helped spur the Golden Age of Biomedicine.
“The main thing we show econometrically, empirically in the paper,” Sampat says, “is that the scientific and technical fields that were the subject of CMR investments during World War II, even after that funding stopped, they continued to grow at a disproportionate pace for the next 10, 20, 30 years.”
How Europe’s data privacy protection law has affected clinical trials
The European Union’s General Data Protection Regulation is the world’s most comprehensive data privacy regulation. The working paper, “Data Privacy Regulation and Innovation,” by Sukhan Kang and Jennifer Kao, considers how the GDPR has affected pharmaceutical innovation.
Since it took effect in 2018, economists have researched the GDPR’s impact on the tech world. But there’s been less focus on the GDPR’s role in medical research. Medical data is, obviously, sensitive; the healthcare sector’s been one of the most vulnerable to data breaches. But one tradeoff is that medical research depends on data sharing.
Researchers often use data from one trial to inform a subsequent trial. But the GDPR limits this kind of data sharing. With the GDPR, patients could request that their data be deleted. Compliance could be more costly. “The GDPR makes it more challenging to run more complicated trials that involve multiple research partners, that involve multiple clinical trial sites,” Kao says.
Kang and Kao look at how the GDPR has reshaped clinical trials in Europe since the law took effect. They show: the total number of trials went down by 18% at the impacted companies. Trials were less likely to take place in the EU generally. The ones that did focused on fewer diseases, took longer to complete, and were less likely to be completed.
Kang and Kao also saw that the number of research collaborations between firms went down, especially when those collaborations involved younger companies — in part because they had fewer resources to manage the greater compliance costs.
“An unintended consequence is that this regulation might disproportionately impact smaller, younger firms relative to older, larger ones,” Kao says. “Our results are not arguing against regulation. But they highlight the importance of designing policies that protect patient data while also not slowing down medical innovation."
Much of what we buy has tons of component parts, made in various different countries – and these days, many of them may have their own tariff rate. It’s part of why a designation like “Made in China” or “Made in the USA” is so significant. (We did a whole podcast about this.) At what point does a product become “Chinese,” or when is it “American”? These questions have bigger stakes when tariffs are involved.
In “The contribution of imports to consumer prices,” economists Omar Barbiero and Hillary Stein (who work at the Federal Reserve Bank of Boston, though their research doesn’t reflect the Boston Fed’s views) have a new way of looking at how prices get influenced by tariffs on intermediate goods. They look at the “border price” of a good (or how much it costs when it’s at the border), how much a tariff may change that, and then how that filters through the rest of the economy.
APU GOMES/Getty Images
The researchers studied this by looking at the 2018 tariffs. “The ways in which a tariff increase impacts the final consumer is complex and hard to measure,” the authors note in the paper. And they say it largely depends on how exactly economists measure the impact of intermediate goods on a final price.
With new measurement tools, Barbiero and Stein mapped how a tariff at the border wound up affecting what consumers paid for them in the wake of the 2018 tariffs — and then, factoring in tariffed intermediate goods, how the cost of the tariff trickled through the economy.
“People always expect to hear that Americans pay more for tariffs, but is it American companies or American consumers? The 2018 tariffs were smaller than the ones today,” Dr. Barbiero tells us. “But we find that consumers were still the ones who bore the brunt of the tariffs.”
Most of the economic literature focuses on people with physical disabilities, or people who become disabled later in life. There’s very little about people with cognitive disabilities at all.
Though a relatively small group, people with cognitive disabilities are a growing share of working age Americans. The Census defines a cognitive disability as “having serious difficulty concentrating, remembering, or making decisions.” This definition would include ADHD, learning disabilities, autism, and bipolar disorder. People with cognitive disabilities tend to have lower levels of education, income and employment, and are more likely to get paid the minimum wage or close to it, compared to people without reported disabilities.
So, what the researchers looked at was: how have recent state-level minimum wage increases impacted people with cognitive disabilities? After all, 30 states have minimum wages higher than the federal minimum, many of which took effect in 2019.
What they found was kind of a mixed bag. When the minimum wage increased by one dollar, that led to a “2.5 to 5.1% decrease in employment among adults with cognitive disabilities.” The effects were most extreme for people with the least education.
Why that decline happened – it’s hard to say for sure, but the authors have a few theories. It’s possible some workers chose to leave the labor force, because that slight increase in pay could affect their eligibility for other programs like Medicaid, SNAP, or forms of Social Security if they received it.
Competition could be another factor. A higher minimum wage can bring discouraged workers back into the workforce…. which could also make the market for minimum wage jobs more competitive, making it harder for lots of people, including those with cognitive disabilities, to get jobs. Too many people competing for not enough jobs. Another theory, though a harsh one: it’s possible that employers didn’t want to hire people with cognitive disabilities if they needed to pay a higher minimum wage.
Another finding from the paper, though not necessarily related to the minimum wage – the share of working adults with cognitive disabilities overall increased from 33% to 49% during the time period studied, compared to 81 to 83% of non-cognitive disabilities adults. Despite the minimum wage finding, there has been an overall uptick in employment for those with cognitive disabilities.
Subscribe to Planet Money+ for bonus episodes with behind the scenes takes, extended interviews, and extra facts we couldn’t fit into the main show. Plus, it’s ad free. You’ll get The Indicator and Planet Money Summer School too - all while supporting our nerdy, ambitious journalism. Learn more at Plus.npr.org/PlanetMoney and start with a free 30-day trial by using the code "freemonth".
This housing program helped kids escape poverty — by changing who they befriended — In the 1990s, Congress created HOPE VI, a program that demolished old public housing projects and replaced them with more up-to-date ones. But the program went further than just improving public housing buildings. HOPE VI was designed to transform neighborhoods with concentrated poverty into neighborhoods that attracted people with different incomes. Some people who moved to HOPE VI neighborhoods earned too much to qualify for public housing. And some even paid for market-rate housing. The idea was that this would help create new opportunities for the low-income people who lived there and even lift people out of poverty. Listen here
A huge EU-India deal, Heated Rivalry, and a hefty $200k to Olympians — It’s … Indicators of the Week! The Indicator looks at some of the most fascinating economic numbers from the news. Listen here
