Macro Roundup

Jason Del Rey, "Leaked Amazon memo warns the company is running out of people to hire,"Vox, June 17, 2022, https://www.vox.com/recode/23170900/leaked-amazon-memo-warehouses-hiring-shortage

Leaked Amazon memo warns the company is running out of people to hire

Amazon is facing a looming crisis: It could run out of people to hire in its US warehouses by 2024, according to leaked Amazon internal research from mid-2021 that Recode reviewed. If that happens, the online retailer’s service quality and growth plans could be at risk, and its e-commerce dominance along with it.

Raising wages and increasing warehouse automation are two of the six “levers” Amazon could pull to delay this labor crisis by a few years, but only a series of sweeping changes to how the company does business and manages its employees will significantly alter the timeline, Amazon staff predicted.

“If we continue business as usual, Amazon will deplete the available labor supply in the US network by 2024,” the research, which hasn’t previously been reported, says.

The report warned that Amazon’s labor crisis was especially imminent in a few locales, with internal models showing that the company was expected to exhaust its entire available labor pool in the Phoenix, Arizona, metro area by the end of 2021, and in the Inland Empire region of California, roughly 60 miles east of Los Angeles, by the end of 2022. Amazon’s internal report calculated the available pool of workers based on characteristics like income levels and a household’s proximity to current or planned Amazon facilities; the pool does not include the entire US adult population.

Amazon spokesperson Rena Lunak didn’t refute the contents of the internal report Recode obtained but declined to comment on it.

The research provides a rare glimpse into the staffing challenges that Amazon is now facing behind its slick veil of one-click online shopping and same-day Prime delivery. And it pointedly reveals how much of Amazon’s business success and its longtime position as a darling of Wall Street investors is dependent on its workforce of more than 1 million people who pick, pack, and ship its customers’ orders nearly 24/7.

The leaked internal findings also serve as a cautionary tale for other employers who seek to emulate the Amazon Way of management, which emphasizes worker productivity over just about everything else and churns through the equivalent of its entire front-line workforce year after year.

In the past, that churn wasn’t a problem for Amazon — it was even desirable at some points. Amazon founder and former CEO Jeff Bezos saw his warehouse workforce as necessary but replaceable, and feared that workers who remained at the company too long would turn complacent or, worse, disgruntled, according to reporting by the New York Times. But now, as the internal report Recode reviewed shows, some inside Amazon are realizing that strategy won’t work much longer, especially if leaders truly want to transform it into “Earth’s best employer,” as Bezos proclaimed in 2021.

To be sure, part of Amazon’s turnover issue relates to how some employees view working in a warehouse as a brief pit stop on the way to better things. But some workers have long complained of stresses unique to Amazon’s workplace, from the pace and repetition of the labor to the unrelenting computerized surveillance of workers’ every move to comparatively high injury rates. In a company survey of 31,000 workers who left Amazon that was referenced in the report, some former Amazon workers say it’s worse to work at Amazon than some big-name competitors like Walmart or FedEx. In that survey, those who joined another employer soon after leaving the tech giant “rated Amazon significantly worse on work fitting skills or interests, demands of the work, shift length and shift schedule.”

With traditional competitors ramping up their investments in e-commerce warehouses, Amazon is no longer a slam-dunk top choice for those seeking work in these types of facilities and the starting minimum wage that comes along with it. And that dynamic is already playing out in some parts of the country.

Danger zones

In the Inland Empire region of California, for example, Amazon may cycle through every worker who’d be interested in applying for a warehouse job by the end of 2022, the internal report warned. One of the reasons is that Amazon is increasingly finding itself in a bidding war for workers with rivals in the area, which is a key logistics region because it is within a two-hour drive of 20 million potential customers and two of the largest container ports in the US.

“We are hearing a lot of workers say, ‘I can just go across the street to Target or Walmart,’” said Sheheryar Kaoosji, co-executive director of an Inland Empire nonprofit called the Warehouse Worker Resource Center. Kaoosji added that Walmart is offering some workers with past warehouse experience as much as $25 an hour. An Amazon executive told Reuters in late 2021 that the company was bumping the average starting wage for new hires in the US to more than $18 an hour, attributing the decision to intense competition among employers. He also said Amazon had increased hiring bonuses to as much as $3,000 in some geographies.

And internal forecasts showed the situation was dire in Phoenix, Arizona, with Amazon projected to exhaust its entire potential workforce by the end of 2021. The Phoenix metro area has been a key market for Amazon since it opened its first warehouse there in 2007. The company currently operates more than 20 facilities in the region. But attrition at Amazon’s facilities in the area grew from 128 percent in 2019 to 205 percent in 2020, as the pandemic upended labor markets and online shopping boomed, putting pressure on fulfillment center employees.

As a result, Amazon seemed to have reversed, or stopped enforcing, some workplace policies at Phoenix warehouses amid the labor shortage, according to a former manager.

“They were so concerned about attrition and losing people that they rolled back all the policies that us as managers had to enforce,” Michael Garrigan, a former entry-level manager at Amazon warehouses in Phoenix from 2020 to early 2022, told Recode. “There was a joke among the … managers that it didn’t matter what got written up for because we knew HR was gonna exempt it. It was almost impossible to get fired as a worker.”

Lunak, the Amazon spokesperson, declined to comment on Garrigan’s claims.

The internal research also identified the regions surrounding Memphis, Tennessee, and Wilmington, Delaware, as areas where Amazon was on the cusp of exhausting local warehouse labor availability. Amazon’s models used for this internal research were 94 percent accurate in predicting the US geographies where Amazon was significantly understaffed in the lead-up to the Amazon Prime Day shopping event in June 2021, the report noted, which contributed to delivery delays for customers in those markets. The warnings about Amazon’s labor supply shortages indicate that in at least some markets, Amazon shipments could face more severe delays in the future.

Despite its looming labor crisis, Amazon temporarily overcorrected in some markets, going from understaffed to overstaffed. Amazon’s chief financial officer had previously said that the company was understaffed by 10,000 employees during the end of 2021, before the omicron Covid-19 variant had wreaked havoc on much of the US. But in April, the company revealed that it was actually overstaffed in some areas in early 2022 as the first wave of omicron subsided and employees returning from sick leave worked alongside new hires who had been recruited to backfill their roles.

Amazon spokespeople have said that the company will count on natural attrition rates to solve much of the current overstaffing problem, and the Wall Street Journal reported on Thursday that a top company official pitched a plan internally to “ out its worker base through attrition.” It’s unclear where exactly Amazon is overstaffed and how long it will take to rightsize its workforce, but it seems unlikely that it is thinning staff in competitive locales like Phoenix and the Inland Empire where it had already exhausted much of the labor pool. It’s also unclear how the current economic climate will impact consumer spending and, relatedly, Amazon’s hiring needs.

For better or worse, the approach of reducing the temporary overstaffing issue through attrition should work for Amazon because it has long churned through its workers at a rapid clip. Amazon’s attrition rates were 123 percent in 2019 before jumping to 159 percent in 2020, according to internal data in the report Recode obtained, while turnover rates across the US transportation and warehouse sectors were much lower: 46 percent and 59 percent respectively in 2019 and 2020, according to Bureau of Labor Statistics estimates.

Turnover in the US retail industry was slightly higher than that — 58 percent and nearly 70 percent respectively in 2019 and 2020 — but still only about half as bad as Amazon’s. The high rates of attrition “made some executives worry about running out of workers across America,” the New York Times reported in 2021, though the article did not include specific timelines.

The leaked report viewed by Recode reads like an attempted wake-up call — along with potential solutions to avert the crisis — for some company leaders who long exhibited a nonchalant attitude toward employee attrition.

No silver bullet

Amazon has a variety of potential solutions for its people problem, but they will require the company to shift its mindset and overcome practical or logistical challenges.

On the surface, simply employing its current workers for longer would be a big help. The turnover rate disparity between Amazon and industry averages shows there is ample opportunity for the company to keep employees longer and delay the arrival of the day when it won’t have workers left to recruit. This is not some unsolvable, mysterious problem; the BLS stats show that plenty of companies retain workers much better than Amazon does. In fact, Amazon’s own data shows that nearly 90 percent of new workers say they want to stay at their jobs for at least six months. If Amazon could bring attrition rates down to its 2019 levels, which were still above 100 percent, the company would gain three more years of hiring runway, according to the internal projections.

In other parts of the country, though, where labor shortages aren’t yet a certainty, remnants of Amazon’s longtime aggressive termination practices persist. It’s not uncommon for some of Amazon’s automated computer systems to automatically fire employees for a variety of minor infractions, without exception. Jose Pagan, a former Amazon employee at a warehouse in Bronx, New York, says he got the automated ax recently despite nothing but positive feedback from his managers.

Pagan began working at the Amazon delivery hub in October and, within two months, had been promoted to a role on the safety committee for the facility. The new role didn’t come with a pay raise, and is on top of a worker’s core tasks, but Pagan saw it as a stepping stone to an official promotion. But in April, Pagan told Recode, he took two days off to have an infected tooth looked at and ultimately removed.

The problem, he said, was that he only had seven hours of unpaid time off but ended up missing 20 hours of work; he had enough paid vacation time to cover the absence, but he said the company did not pull from that separate bank of days because Pagan would have had to apply for vacation time in advance. Pagan said he also had a doctor’s note but was told the company did not need to accept it as an excuse, even though he had been excused from work with a doctor’s note previously. He said he worked for another full week without issue, until he showed up one night for his overnight shift and his badge no longer worked. He was eventually told he had been terminated.

An HR manager told Pagan that there was nothing he could do about the termination but that Pagan should reapply for a job at the company in three months, per Amazon policy.

“We would love you back in 90 days,” Pagan says the HR staff member told him. In the meantime, Pagan should “do some GrubHub or Uber,” the HR employee said.

“I find the whole situation crazy,” said the 35-year-old Pagan, who was supporting his wife and daughter on his Amazon income. “They’re gonna lose a good worker for nothing.”

Lunak, the Amazon spokesperson, said the company is looking into Pagan’s case.

Besides changing termination or retention policies, increasing pay is another obvious lever that Amazon could pull to expand its labor pool. (All of these are things that workers calling for unionization have demanded from the company.) The report predicted that for every dollar Amazon bumps up its minimum wage, it adds 7 percent more workers to its potential hiring pool. If Amazon were to do a little better, and raise its hourly minimum by just $1.50, that too would expand its pool of potential workers enough to extend its hiring ability in the US by three years.

The internal document also suggested Amazon needs to become more efficient at hiring. At the time the report was written, Amazon needed 6.7 job applicants to apply to fill a single warehouse role. Around 9 percent of applicants were rejected either because they were former employees not permitted to be rehired or because they failed a drug test or had an unsatisfactory background check. (Later in 2021, Amazon said it would stop screening many of its warehouse worker applicants for marijuana use.)

Of course, Amazon could also simply reduce the number of workers it needs by speeding up automation in its warehouses — a controversial approach. Nonetheless, the report revealed that Amazon executives had already in 2021 set a “conservative” goal of improving warehouse productivity by 25 percent by the end of 2024, strictly through increased automation. Hitting that goal on its own would push back the labor crisis as well, but only slightly.

The research team also pondered improvements that could be made to how Amazon already utilizes its existing staff. Amazon’s warehouse staff worked, on average, a little more than 27 hours a week in 2020, according to internal data. If Amazon had increased that weekly number by just 10 percent, the company could have reduced new hires by 118,000 people, the internal report estimated. The report also referenced a team in Amazon’s HR division, called Hamilton, that is building tools to automatically transfer workers between nearby facilities based on staffing levels and order volume. The relocations would come in the form of both “permanent transfers and short-term assignments.”

Lastly, according to the report, Amazon’s HR staffing division wants to play a bigger role in influencing where new warehouses are located so they can ensure the available labor pool is large enough for the company’s needs. For some types of Amazon warehouses, there is little wiggle room. Amazon delivery stations, for example, are the last stop for a package before it’s delivered, so they need to be located within a short driving distance to a large number of Amazon customers. But for others — like “cross dock” facilities that receive merchandise from suppliers, and fulfillment centers that receive goods from cross dock facilities and pack them into customer orders — there is more leeway in the location selection process and so an opportunity to better use internal labor forecasting tools.

“Our longer-term strategy … is to apply labor forecasts to future site selection,” the report read.

Overall, the leaked report offers a variety of solutions to choose from, but each with trade-offs that Amazon executives may not find palatable. Raise wages and the company may need to pull back spending elsewhere. Increase automation and risk the wrath of critics concerned about replacing people with robots at the second-largest private sector employer in the country. A focus on better retaining employees could also mean reducing performance tracking and productivity quotas that have played a role, however controversial, in the company’s historic business success to date.

Amazon has shown time and time again that it values “customer obsession” — and the promises it makes to its customers — above all else. But the customer loyalty that results from that obsession is ultimately at risk if Amazon cannot employ enough people — or robots — to pack and ship the boxes people expect to find outside their front door a day or two after clicking “Place your order.” The company’s new CEO, Andy Jassy, has proclaimed that Amazon is “not close to being done in how we improve the lives of our employees.” As the internal report shows, doing so should no longer be optional for Amazon; it’s an imperative.

“…Figure 3 shows real wage growth for the median worker and at other points in the distribution. Since 1990, real median wages grew by 34 percent. Real wages at the tenth, twentieth, and thirtieth percentiles grew over this period by 50, 48, and 38 percent, respectively….”

Michael Strain, "Have Wages Stagnated for Decades in the US?,"American Enterprise Institute, June 27, 2022, https://www.aei.org/articles/have-wages-stagnated-for-decades-in-the-us/

Have Wages Stagnated for Decades in the US?

In the United States, it is conventional wisdom that wages have stagnated for decades.

A few examples to illustrate the point: In a recent story in “The New York Times’s” Business section, an article began with this statement: “One of the most urgent questions in economics is why pay for middle-income workers has increased only slightly since the 1970s, even as pay for those near the top has escalated” (Scheiber, 2021). Conservative commentator David Brooks declared in 2017 that “middle-class wage stagnation is the biggest economic fact driving American politics.” A 2018 analysis by the Pew Research Center had this headline: “For most U.S. workers, real wages have barely budged in decades.” Populist politicians on the political right and progressives on the political left share this view, as well.

Many economists agree. Economists assume wage stagnation as a fact in commentary written for the public (e.g., Krugman, 2021; Stiglitz, 2020) and in academic and policy papers (e.g., Azar et. al, 2020; Hoynes and Rothstein, 2019; Sachs et. al, 2015; Benmelech et. al, 2022; Benmelech et. al, 2019).

Ephrem Ravi and Krishan M Agarwal, "Quantifying the commodity consumer shock - at ~5-6% of global GDP it’s as big as the early 1970’s,"Citi, June 14, 2022, https://www.citi.com/mss/solutions/citi-research/

Global consumers of commodities are set to pay producers ~$5.2tr more during 2022 than they did in 2019, an increase representing ~5.0% of world GDP, using Citi’s base case 2H’22 forecasts. In the scenario that 2H’22 forward prices materialise, the commodity ‘tax’ paid by consumers would be ~$6.3tr higher than during 2019, equal to ~6.2% of GDP. In either case, the ongoing commodity shock is on track to be a similar order of magnitude as the first oil shock almost 50 years ago, when taken as a share of global GDP.The longer the commodity shock persists, the bigger the negative impact on commodity consumers and on net, global growth and equities. We find that Europe and some EMs growth looks particularly vulnerable, as does € credit, to a higher for longer commodity price scenario, while South American FX should benefit. For OECD Europe, consumers of commodities could pay $1.4tr more during 2022 than they did in 2019, with commodity consumption costs rising to $2.3tr from $0.9tr, representing an increase of -5.9% of OECD Europe GDP, comparing May spot (annualised, to 2019, or using 2H'22 forwards). The value of commodities consumed in Europe is estimated to have been higher during May 2022 (annualised), at 10.3% of GDP, than it was during the peak of the 2000's commodity supercycle period, at 8.2% of GDP.Europe is a major net energy importer. For the US. consumers of commodities could pay $1.0tr more during 2022 than they did in 2019, with commodity consumption costs rising to $1.8tr from $.8tr representing an increase of -3.4% of US GDP, comparing May spot (annualised to 2019)The US is a net energy and agricultural commodity exporter.

This paper shows that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, we train a machine learning model to predict the risk of being shot in the next 18 months. We address central concerns about police data and algorithmic bias by predicting shooting victimization rather than arrest, which we show accurately captures risk differences across demographic groups despite bias in the predictors. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, 13 percent are shot within 18 months, a rate 130 times higher than the average Chicagoan. Although Black male victims more often have enough police contact to generate predictions, those predictions are not, on average, inflated; the demographic composition of predicted and actual shooting victims is almost identical. There are legal, ethical, and practical barriers to using these predictions to target law enforcement. But using them to target social services could have enormous preventive benefits: predictive accuracy among the top 500 people justifies spending up to $123,500 per person for an intervention that could cut their risk of being shot in half.

Sara Heller, Benjamin Jakubowski, Zubin Jelveh and Max Kapustin, "Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It,"National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30170

“…Figure 2 reports two measures of model performance across the predicted risk distribution. Figure 2a shows Precision:, or the share of people who are actually shot during the 18-month outcome period among the: people with the highest predicted risk Figure 2b shows Recall:, or the share of actual shooting victims during the 18-month outcome period who are among the: people with highest predicted risk: We show two versions of recall in Figure 2b. The first, simply labeled recall, uses the total number of shooting victims in the prediction sample as the denominator, or 2,253. The second, labeled total recall, uses the total number of shooting victims in the entire city during the outcome period as the denominator, or 3,381. The difference between these two highlights a point we return to in the following section about whom predictions based on police data miss: one-third of eventual shooting victims are not in our prediction sample and therefore not assigned a predicted risk by the model. Though it is more common when evaluating the performance of a predictive algorithm to report recall, total recall helps to assess the ability of algorithmic prediction to identify shooting victims city-wide, regardless of whether they have enough prior police contact to be included in the prediction sample. The share of people shot during the 18-month outcome period is startlingly high among those in the right tail of the distribution (Figure 2a). Among the: = 500 people with highest predicted risk, 13 percent, or 65 people, are shot. This is almost 19 times higher than the base victimization rate for the prediction sample (327,127 people) of 0.7 percent, and 130 times the city-wide victimization rate (2.7 million people) of 0.1 percent. Among the: = 3, 381 people with highest predicted risk—corresponding to the actual number of shooting victims during the 18-month outcome period—almost 9 percent are shot. Those at higher predicted risk for shooting victimization are also at significantly elevated risk for other adverse outcomes, like shooting arrest and violent victimization The recall rates confirm that those in the right tail of the distribution account for an outsized share of all shooting victims (Figure 2b). Despite representing just under 0.02 percent of the city’s population, the: = 500 people with highest predicted risk include almost 2 percent of the 3,381 total victims during the 18-month outcome period.19 The: = 3, 381 people with highest predicted risk—just over 0.1 percent of the city’s population—include almost 9 percent of total victims….”

“….we build a model to predict shooting victimization in Chicago over an 18-month period. The model uses arrest and victimization records for 643,914 people from the Chicago Police Department (CPD), including over 1,400 predictors that capture a person’s demographic information, arrest and victimization histories, and the arrest and victimization histories of peers who were co-involved in prior criminal incidents….”

Results

“…First, the model successfully identifies a small group of people at extraordinarily high risk of being shooting victims. Of the 500 people at highest predicted risk, 13 percent are actually shot during the following 18 months—a rate almost 19 times higher than everyone in our prediction sample of people with recent police contact (0.7 percent across 327,127 people) and 130 times higher than everyone in Chicago (0.1 percent). An intervention that could cut the risk of being shot in half for these 500 people would generate an estimated social cost savings of $62 million from the victimization reduction alone (Cook and Ludwig, 2000; Ludwig and Cook, 2001). If the intervention cost less than $123,500 per person, it would pay for itself. Our analysis unpacks what information the model is using to achieve this predictive performance….”

“…Second, the predictions do not misrepresent victimization risk across demographic groups. We show that Black male shooting victims are more likely to have a predicted risk, because they are more likely to have prior police contact.5 This finding highlights how using police data limits an algorithm’s ability to identify future victims with little or no prior police contact. But importantly, the accuracy of predictions is similar, on average, across race, age, and gender groups. The demographic composition of predicted shooting victims matches almost exactly that of actual shooting victims. In other words, the predictions do not disproportionately inflate the victimization risk of Black men….”

Evidence

“…The top left panel of Figure 1 shows the overall distribution of the model’s predictions and how they compare to realized rates of shooting victimization for the prediction sample. The x-axis is the average predicted risk for each percentile of the risk distribution, with each point containing 1 percent of the sample, or 3,271 people. The y-axis is the actual rate of shooting victimization in the 18-month outcome period for the 3,271 people in each bin. Three features about the overall predictions are apparent. First, on average, the model’s risk predictions are accurate (well-calibrated): their slope is close to the 45-degree line, albeit with some under-prediction for people in the right tail and some over-prediction for people in the highest-risk bin. Second, the vast majority of people in the sample are predicted to have a shooting victimization risk close to zero, as indicated by the mass of points in the bottom left of the graph. Finally, the predicted risk distribution is highly positively skewed, with points in the upper right of the graph corresponding to a small group of people in the long right tail whose predicted risk of being shot in the 18-month outcome period is very high….”

Data

Economic models of fertility behaviour relied on two main ideas to account for empirical regularities of fertility choice. The first of these is the quantity-quality trade-off: the notion that as people get richer, they invest more in their children’s ‘quality’, in particular by providing them with more education. The second main idea centred on women’s opportunity cost of time. According to this mechanism, children are more ‘expensive’ when women’s wages are high and many women work, because raising children and working are competing uses of women’s time. Relying on these mechanisms, first-generation models of fertility behaviour were able to account for the empirical regularities that held across a wide set of countries until a few decades ago, particularly the observation that fertility rates were lower in countries that are richer and where many women work. The forces emphasised by first-generation models remain relevant in many places, particularly in countries that are still in the midst of their demographic transition. However, as we argue in a survey of the recent literature on the economics of fertility (Doepke et al. 2022), the classic ideas of the first-generation models of fertility are of little help in understanding ultra-low fertility rates in high income countries today because the basic observations that motivated first-generation models no longer hold in recent data. As a result, the economics of fertility has entered a new era, in which a new set of forces drive much of the observed variation in fertility. Figures 1 and 2 show how fertility in high-income countries has changed. In 1980, fertility was still declining in GDP per capita across OECD economies; by 2000, it was the richer countries that had more children. Similarly, Figure 2 shows that in 1980 in the OECD, the countries with the highest female labour-force participation had the lowest fertility rates. By the year 2000, this relationship had reversed - fertility is now highest in countries where many women work.The biggest challenge in combining a career and family is dealing with childcare needs. If women end up having to provide most of the childcare on their own, continuing in a demanding career while having young children will be difficult or impossible. A common alternative mode of childcare is provided by day-care centres and preschools, which can be public or private. If such childcare is widely available, covers all of the working day, and is affordable, women with young children have an easier time continuing to work and might be more likely to have larger families as a result (Del Boca 2002, Apps and Rees 2004). In line with this intuition, Figure 3 (which is based on Olivetti and Petrongolo 2017) shows that public spending on early childhood education is closely related to both fertility rates and women’s employment across countries. The countries with the lowest total fertility rates (below 1.5) are all in the bottom half of early childhood education spending. If men contribute little to raising children, women will be less likely to agree to another child, and fertility will be low. Consistent with this view, Figure 4 displays a strong cross-country correlation between men’s contributions to childcare and housework and the total fertility rate. In all countries with a fertility rate below 1.5 (blue dots), men do less than a third of the work in the home.

Matthias Doepke, Anne Hannusch, Fabian Kindermann and Michèle Tertilt, "A new era in the economics of fertility,"Center For Economic And Policy Research, June 11, 2022, https://voxeu.org/article/new-era-economics-fertility

A new era in the economics of fertility

Governments in many high-income countries are grappling with rapid population ageing driven by ultra-low fertility rates. In Germany, Italy, Spain, and Japan, fertility rates have been below 1.5 children per woman for more than two decades, implying that each new generation is less than three-quarters the size of the preceding generation. In East Asian countries such as South Korea, fertility rates are below one child per woman. Even in the US, which maintained fertility rates above the high-income country average for some time, fertility rates have recently dropped to about 1.6.

How can we make sense of why fertility rates have dropped, and how can we assess which measures, if any, might contribute to higher fertility rates in the future?

Starting with Becker (1960), economic models of fertility behaviour relied on two main ideas to account for empirical regularities of fertility choice. The first of these is the quantity-quality trade-off: the notion that as people get richer, they invest more in their children’s ‘quality’, in particular by providing them with more education. Given that education is costly, parents choose to have fewer children as incomes rise. The second main idea centred on women’s opportunity cost of time. According to this mechanism, children are more ‘expensive’ when women’s wages are high and many women work, because raising children and working are competing uses of women’s time. Relying on these mechanisms, first-generation models of fertility behaviour were able to account for the empirical regularities that held across a wide set of countries until a few decades ago, particularly the observation that fertility rates were lower in countries that are richer and where many women work.

New fertility facts

The forces emphasised by first-generation models remain relevant in many places, particularly in countries that are still in the midst of their demographic transition. However, as we argue in a survey of the recent literature on the economics of fertility (Doepke et al. 2022), the classic ideas of the first-generation models of fertility are of little help in understanding ultra-low fertility rates in high income countries today because the basic observations that motivated first-generation models no longer hold in recent data. As a result, the economics of fertility has entered a new era, in which a new set of forces drive much of the observed variation in fertility.

Stepping back, I’m claiming that science is getting harder, in the sense that it is increasingly challenging to make discoveries that have comparable impact to the ones in the past. Diverse groups – the Nobel nominators, contemporary surveyed scientists, academics, and inventors – all seem to have an increasing preference for the work of the past, relative to the present. Prior to the 1970s, on average 90% of the time, awards went to papers published in the last twenty years. But by 2015, the ten-year moving average was closer to 50%. So recent discoveries seem to have a harder time getting recognized as Nobel-worthy, relative to a few decades ago.And looking at growth in the number of topics covered by scientists also suggests it has become harder to make forward progress. To close, I’ll add two more arguments. First, we should expect science to get harder because of the “burden of knowledge.” The basic idea is, almost as a tautology, making new discoveries requires new knowledge; otherwise the discovery would likely already have been made. Whenever new knowledge is discovered, it opens the way for new discoveries, and it may also displace or make obsolete some older knowledge. But if new knowledge does not entirely displace old knowledge, then it may be you need steadily more knowledge to make new discoveries. Unfortunately, I think we have quite good evidence this seems to be the case as a general (though probably not absolute) rule, much of it reviewed here. All else equal, if you need more and more knowledge to make a discovery of a given size, then you can probably expect discoveries of a given size to require more time or manpower to bring about. And the evidence of this post suggests that is what we have in fact observed. Second, if it’s true that innovation in other domains has in fact gotten harder (and I think it has), then to the extent scientific discovery and other forms of innovation are a similar process we shouldn’t be surprised when what applies to the former also applies to the latter. As noted at the outset of this post, if you pick some metric of technological progress, odds are that a constant rate of progress along that metric is accompanied by rising R&D effort. Why should science be so different? Each of these pieces of evidence has holes in it. But I think they are not the same holes. Stack them all up, and I think you get an argument that can begin to hold water.

Matt Clancy, “Science is getting harder,”What’s New Under the Sun, June 1, 2022, https://mattsclancy.substack.com/p/science-is-getting-harder

Science is getting harder

One of the most famous recent papers in the economics of innovation is “Are Ideas Getting Harder to Find?” by Bloom, Jones, Van Reenen, and Webb. It showed that more and more R&D effort is necessary to sustain the present rates of technological progress, whether we are talking about Moore’s law, agricultural crop yields, healthcare, or other proxies for progress. Other papers that look into this issue have found similar results. While it is ambiguous whether the rate of technological progress is actually slowing down, it certainly seems to be getting harder and harder to keep up the pace.

What about in science?

A basket of indicators all seem to document a trend similar to what we see with technology. Even as the number of scientists and publications rises substantially, we do not appear to be seeing a concomitant rise in new discoveries that supplant older ones. Science is getting harder.

Before diving into these indicators, I want to head off one potential misunderstanding. My claim is that science is getting harder, in some sense, not that science is ending or that we are on the verge of running out of ideas. Instead, the claim is that discoveries of a given “size” are harder to bring about than in the past.

Raw paper output

We’ll actually start with an indicator that shows no evidence of a slowdown though. Since scientists primarily communicate their discoveries via papers, the first place to look for evidence of increasing difficulty of making discoveries is in the number of papers scientists publish annually. The figure below, drawn from Dashun Wang and Albert-László Barabási’s (free!) book on the Science of Science compares publications to authors over the last century.

At left, we can see the number of papers and authors per year has increased basically in lockstep over the twentieth century. Note, the axis is a log-scale, so that a straight-line indicates exponential growth. Meanwhile, at right, the blue dashed line shows that the number of papers per author has hovered around 2 for a century and rather than falling, it is actually on the rise in recent decades. (As an aside, the solid red line at right is strong evidence for the rise of teams in science, discussed more here)

So absolutely no evidence that scientists are struggling to find stuff worth writing up. But that’s not definitive evidence, because scientists are strongly incentivized to publish and what constitutes a publishable discovery is whatever editors and peer reviewers think is publishable. If fewer big discoveries are made, scientists may just publish more papers on small discoveries. So let’s take a more critical look at the papers that get published and see if there are any indicators that they contain smaller discoveries than in the past.

Nobel Prizes

Let’s start by looking at some discoveries whose importance is universally acknowledged. The Nobel prize for discoveries in physics, chemistry, and medicine is one of the most prestigious scientific prizes and has a history long enough for us to see any long-run trends. Using a publicly available database on Nobel laureates by Li et al. (2019), we can identify the papers describing research that is eventually awarded a Nobel prize, and the year these papers were published. Note several papers might be associated with any given award. For each award year, we can then ask, what share of the papers related to the discovery were published in the preceding twenty years. The results of that are presented below, though I smooth the data by taking the ten-year moving average.

Prior to the 1970s, on average 90% of the time, awards went to papers published in the last twenty years. But by 2015, the ten-year moving average was closer to 50%.

So recent discoveries seem to have a harder time getting recognized as Nobel-worthy, relative to a few decades ago. We can also compare the importance of different discoveries that won Nobel prizes. In 2018, Patrick Collison and Michael Nielsen asked physicists, chemists, and life scientists to pick the more important discovery (in their field) from sets of two Nobel prize winning discoveries. For example, they might ask a physicist to say which is more important, the discovery of Giant Magnetoresistance (awarded the Nobel in 2007) or the discovery of the Compton effect (awarded in 1927). For each decade, they look at the probability a randomly selected discovery made in that decade would be picked by their survey respondents over a randomly selected discovery made in another decade. The results are below:1

A few points are notable from this exercise. First, physicists seem to think the quantum revolution of the 1910s-1930s was the best era for physics and it’s been broadly downhill since then. That’s certainly consistent with discoveries today being in a sense smaller than the ones of the past, at least for physics.

In contrast, for chemistry and physiology/medicine, the second half of the twentieth century has outperformed the first half. In the Nobel prize data, within the second half of the century, there is no obvious trend up or down for chemistry and medicine. While that’s better than physics, it remains consistent with the notion that science might be getting harder. As we can see in the first figure here, the number of papers and scientists rose substantially between 1950 and 1980, which naively implies that the number of candidates for Nobel-prize winning discoveries should also have risen substantially. If we are selecting the most important discovery from a bigger pool of candidates, we should expect that discovery to be judged more important than discoveries picked from smaller pools. But that doesn’t seem to be the case.

So Nobel prize data is also consistent with the idea that discoveries today aren’t what they used to be. Whereas it used to be quite common for work published in the preceding twenty years to be recognized for a Nobel, that doesn’t happen nearly so much today. That said, an alternative explanation is that the Nobel committee is just trying to work through an enormous backlog of Nobel-worthy work which they want to recognize before the discoverers die. In this explanation, we’ll eventually see just as many awards for the work of today.

But it’s not clear to me this is how the committee is actually thinking: recent work is awarded half the time still if the committee thinks the discovery is sufficiently important. For example, Jennifer Doudna and Emmanuelle Charpentier were awarded a Nobel for their work on CRISP-R in 2020, less than a decade after the main discoveries. And when you look specifically at the work performed in the 1980s, it doesn’t seem particularly notable, relative to work in the 40s, 50s, 60s, and 70s, despite the fact that many more papers were published in that decade.

Top Cited Papers

Still, perhaps the Nobel prize is simply too idiosyncratic for us to learn much from. Next, let’s look at another indicator of big discoveries, one which shouldn’t be biased by the sort of factors peculiar to the Nobel. This is the most top-cited papers in a given field. For example, if we look at the top 0.1% most highly cited papers of all time in a particular field, we could ask how easy is it for a new paper to join their ranks. If that has fallen over time, then that’s further evidence that today’s papers aren’t making the same contributions as yesterday’s.

On the other hand though, we might think it should get harder and harder to climb to the top 0.1%, even if discoveries are not getting smaller. After all, if discoveries are of constant size, earlier works have more time to get citations; it may not be possible for later papers to catch up, even if they are just as good. But there are also some factors that lean in the opposite direction. First, if work is only cited when relevant, then newer work should have an easier time being relevant to newer papers. Since the number of new papers grows over time, that gives one advantage to the new; they can be tailored to a bigger audience, in some sense. Second, the most esteemed papers of all time may actually stop being cited at high rates, because their contributions become part of common knowledge: it is no longer necessary to cite Newton when talking about gravity, or even Watson and Crick when asserting DNA has a double-helix shape.

So let’s proceed with seeing if there has been any change in how easy or hard it is to become a top cited paper, noting that won’t be the last piece of evidence we look at.

The closest paper I know of that looks into this is Chu and Evans (2021), which looks at the probability of a new paper ever becoming one of the top 0.1% most cited, even for just one year. But this paper does not plot this probability against time, like the previous charts: instead, it plots this probability against the size of a field, measured by the number of papers published per year. In the scatterplot below, each point corresponds to a field in a year. On the horizontal axis is the number of papers published in the fields in that year and on the vertical axis the probability a paper in that field and year is ever among the top 0.1% most cited. The colored lines are trends for each of these ten fields. Note this figure only includes papers published in the year 2000 or earlier. Since the analysis is conducted with data from 2014, every paper has more than a decade to accrue citations and get into the top 0.1%.

The figure shows pretty clearly that as fields get bigger the probability of jumping to the top 0.1% shrinks. It used to be the case that papers had a greater than 0.1% chance of being in the top 0.1% at some point. That, itself, suggests some degree of turnover and dynamism at the top of the field. But for the largest fields, that isn’t the case anymore. Note, while this chart has the size of a field on the horizontal axis, since fields tend to get bigger every year, this also shows us trends over time: up until the year 2000, newer papers had successively lower chances of supplanting their rivals and becoming one of the top 0.1% most cited.

Another variant of this chart tells the same story. In the figure below, Chu and Evans find the top 50 most highly cited papers in each year. In red below, they then track the proportion of those papers that stay in the top 50 in the next year. As this moves up, that means fewer and fewer papers are supplanting the top 50 most cited. Again, they plot this against the size of the field rather than time, but since the two move together, it also shows what is happening over time.

(In blue is a related measure, the year-to-year correlation of the rank of top 50 cited papers. In an appendix, they specifically show that this measure is correlated with time alone, so that there is less turnover in more recent years)

If being a top-cited paper is an indicator of large scientific impact, then the above suggests it’s harder to have a big impact than in the past.

Other interpretations are also possible though, such as the factors mentioned earlier. Alternatively, perhaps as fields grow a canon of select papers emerges, and everyone frames their work in relation to this canon, so that earlier work is perpetually cited, but citations don’t accurately capture the “size” of a new discovery. As with the factors peculiar to the Nobel prize, it might be that these two explanations can coexist. But in case there is something strange about how earlier work is necessarily canonized, let’s now turn to some indicators that cover science more broadly.

Growth in Topics

One issue with all the preceding evidence is that the quality of a research discovery is determined by how other scientists assess it: do they cite it, give it prizes, or tell us how important it is relative to alternatives. That might be the best we can do; a thorny problem in research is that evaluating the quality of research often requires the skills to do research, and most of the people with those skills are researchers. But it also means our assessment of the quality of research is tied up with any biases and blindspots a field might have about itself.

So let’s turn to a metric that isn’t based on the assessment of the scientists themselves. Rather than looking at the size of discoveries, we could instead try to chart the topics covered by a field over time. If a field is steadily spreading into new topics (from electrons to quarks to strings, for example), that suggests a field is learning new things and pushing out its frontier. On the other hand, if a field remains stuck on the same set of things (from strings to strings to strings, for example) that might be indicative that the field is struggling to make progress.

Milojević (2015) tries to get at this question by looking to the titles of published papers. Most people will probably only ever read a paper’s title, and so scientists usually try to broadcast what the paper is about with the title.2 Milojević uses the text of titles to define the topics a field is studying. Milojević identifies a topic as a string of words in a title that lies between a grammatical phrase delimiter (think.,:;) or a common English word with non-technical meaning (think “about”, “since”, “using”). When a topic has more than three words, she uses only the last three to define it. As an example, using this technique the most cited paper of all time, “Protein measurement with the Folin phenol reagent” would be construed to be about the topics “protein measurement” and “Folin phenol reagent.”

Now that Milojević has a way to define topics in a field, she then goes about counting how many distinct topics are mentioned in the titles of papers published in a given year. Analogous to the number of papers published per person, Milojević looks at the number of unique topics for every set of 10,000. In other words, the algorithm reads random paper titles until it reaches 10,000 topics, and then counts how many topics from this set of 10,000 are unique.

In 2022, Milojević updated the data from her 2015 paper as part of an OECD workshop (report forthcoming). Below is the number of unique topics studied in a random sample of 10,000 topics across all of science, by year (which Milojevic calls the cognitive extent).

Through the twentieth century, there was a general rise in the number of unique topics studied in a given sample of scientific titles. Over 1935-1975 this rise was a bumpy one, but it looks like we mostly reverted to trend, so the overall rate of change was steady over a long horizon. But sometime since the 1970s, this upward trend has very gradually slowed to a stop, and even began to reverse slightly. If counting topics is a good way to measure the successful growth rate of a field, then this indicates fields are having a harder time growing today than in the past.

Is counting topics in this manner sensible? Carayol, Lahatte and Llopis (2019) provide some complementary evidence on the topics authors are choosing to study using a much more straight-forward data source: the keywords authors supply to publishers to describe their own papers (some other elements of this paper are also discussed here and here). Unfortunately, this data is only available for 1999-2013. Fortunately, that is precisely the period when Milojević begins to observe the sharpest decline in unique topics. So do we see a similar thing when we look at the keywords authors use to describe their papers? Yes.

In the figure below, Carayol, Lahatte, and Llopis compare the growth rate of three different things, but we are most interested in the green dashed line and the blue line. The green dashed line shows growth in the number of publications over 1999-2013 by comparing annual publication to the number published in 1999. The blue line shows the growth in the total number of unique author-supplied keywords used to describe research, again by comparing the number in a given year to the number in 1999. This figure has a log scale, so that a straight line indicates constant exponential growth.

These two curves have begun to diverge. Whereas the growth in the total number of publications has been steady and exponential, growth in the total number of unique keywords has been slower than exponential. That seems broadly consistent with Milojević’s finding that the number of unique topics for a fixed sample size also stopped growing over this period. In other words, in terms of the number of topics being tackled by science, growth of fields is proceeding more slowly today than in the past.

Citations to Recent Papers

Another approach we could take is to compare the citations received by papers over time. If older papers made bigger discoveries than younger ones, then we might expect them to hold on longer and be more highly cited than new papers. One simple way to assess this is to look at the share of citations made in each year to new papers. A simple measure of this is the Price index (named for Derek De Solla Price, not the cost of a good), which computes the share of citations made to papers published in the last 5 years (or 10 years in some variants).

Below, Larivière, Archambault, and Gingras (2007) compute the Price index for all papers on Thompson Scientific over the period 1900-2004. They compute two versions of the index. The 20-year index divides the number of citations to papers published in the preceding 5 years by the number of citations to papers published in the preceding 20 years. The 100-year index does the same thing, but dividing by the number of citations made to papers published in the preceding century.

I think this figure is best understood as describing two periods. From 1900-1955 there was a general increase in the share of citations made to recent work, interrupted by the two world wars. Each world war imposed big disruptions on the production of new scientific work (see the first figure in this post), which had the secondary effect of reducing the share of citations to recent work. But since 1955, the share of citations made to new work has fallen dramatically, by an amount comparable to the distortions caused by the world wars (though spread out over many decades).

Cui, Wu, and Evans (2022) document that this trend has continued until 2014, and is not driven by any single field. It appears to be an almost universal phenomenon. In the figure below, they calculate the share of (all) citations made to work published in the preceding 10 years. It’s down across the board.

This looks pretty alarming, but there are explanations besides a sharp decline in size of discoveries. Whenever the share of something goes down, there are two possible causes: it could be the numerator goes down and/or the denominator goes up. And in this case, it’s mostly the latter. The raw number of citations to recent work doesn’t actually seem to have fallen by very much, at least, according to Larivière, Archambault, and Gingras (2007). But the total number of citations papers make has gone way up, and most of that increase has been citations to older work.

So the real question is less “why have researchers stopped citing new work” and more “why are researchers citing old work at such a high rate.” One explanation is that older work contains the bigger discoveries, and we’re still living in their shadow. But another explantion, put forward by Cui, Wu, and Evans, is simply that the scientific labor force is aging and older scientists are more familiar with older papers. Older scientists might push up the share of citations to older paper either by citing them in their own work, or by insisting on them being cited in other people’s work when they serve as peer reviewers. This age dynamic could also be a factor in the persistence of top-cited papers of the past. But whether this is about the size of discoveries or an aging scientific labor force’s preferences (and the two explanations are not mutually exclusive either), the more distant past is increasingly influential in contemporary science.

So let’s close by looking at one more data source, which is somewhat immune to some of the strategic citation factors unique to academia.

Patent Citations to Recent Papers

Patents also cite academic papers, where relevant. While patents have their own issues, one virtue is that inventors face a different set of incentives than academics. Whereas academics might cite papers or choose to work on topics that they know will be viewed favorably by their older peers, an inventor doesn’t face these constraints. In principle, they might just rely on whatever science is most useful for the purposes of getting some technology to work. Moreover, we have some good evidence science really is a useful input to technology, and that this is reflected tolerably well in citations to science. So if inventors are not citing recent science as much as in the past, that’s another indicator that recent science may be struggling to make comparable discoveries to the papers of the past.

I have not been able to find any papers that look at the extent to which patents cite recent academic research (if you know of such work, please send it my way and I can update this). Fortunately, Marx and Fuegi have a publicly available database of patent citations to academic work, which I used to put together the following figure. In each year of the following figure, I pull all citations made to academic papers by US patents whose application was filed in that year, and that was eventually granted within the next five years. I then compute the share of these citations that go to papers published in the preceding five years. Essentially, this is the Price index, discussed in the previous section, but applied to the citations of patents. I started the figure in 1975, as citations to academic papers were quite rare before then.

This figure echoes what we see in the academic price index: citations to recent work have become increasingly less common. Unlike the academic work though, this is not entirely a story of rising citations to older work and steady citations to new work. Instead, we are actually seeing a decline in the number of citations to recent work. Among the subset of patents that cite academic work, the average number of citations to papers published in five years prior to the patent’s filing date dropped from about 4 around 2000 to under 3.5 by 2015.

The Big Picture

Stepping back, I’m claiming that science is getting harder, in the sense that it is increasingly challenging to make discoveries that have comparable impact to the ones in the past. Diverse groups – the Nobel nominators, contemporary surveyed scientists, academics, and inventors – all seem to have an increasing preference for the work of the past, relative to the present. And looking at growth in the number of topics covered by scientists also suggests it has become harder to make forward progress. To close, I’ll add two more arguments.

First, we should expect science to get harder because of the “burden of knowledge.” The basic idea is, almost as a tautology, making new discoveries requires new knowledge; otherwise the discovery would likely already have been made. Whenever new knowledge is discovered, it opens the way for new discoveries, and it may also displace or make obsolete some older knowledge. But if new knowledge does not entirely displace old knowledge, then it may be you need steadily more knowledge to make new discoveries. Unfortunately, I think we have quite good evidence this seems to be the case as a general (though probably not absolute) rule, much of it reviewed here. All else equal, if you need more and more knowledge to make a discovery of a given size, then you can probably expect discoveries of a given size to require more time or manpower to bring about. And the evidence of this post suggests that is what we have in fact observed.

Second, if it’s true that innovation in other domains has in fact gotten harder (and I think it has), then to the extent scientific discovery and other forms of innovation are a similar process we shouldn’t be surprised when what applies to the former also applies to the latter. As noted at the outset of this post, if you pick some metric of technological progress, odds are that a constant rate of progress along that metric is accompanied by rising R&D effort. Why should science be so different?

Each of these pieces of evidence has holes in it. But I think they are not the same holes. Stack them all up, and I think you get an argument that can begin to hold water.

Dan Frosch, Kris Maher and Zusha Elinson, "Rural America Reels From Violent Crime. ‘People Lost Their Ever-Lovin’ Minds.’,"Wall Street Journal, June 10, 2022, https://www.wsj.com/articles/violent-crime-rural-america-homicides-pandemic-increase-11654864251

Rural America Reels From Violent Crime. ‘People Lost Their Ever-Lovin’ Minds.’

Local prosecutor Rebecca McCoy used to think of her home in central Arkansas as a place where the worst crimes were usually stolen tractors and lawn mowers.

In March 2020, she was called to the trailer of a 72-year-old man who had been bludgeoned to death with a baseball bat. It was White County’s first homicide in almost two years. By that December, there were 11 more.

In Marion County, a swampy stretch of South Carolina, Sheriff Brian Wallace and his deputies worked nine killings in 2021, including the execution-style shooting of an 80-year-old retired teacher whose family the sheriff knew personally. It was the highest annual body count he had seen since he joined the small department more than two decades earlier.

For ranchers Bill and Diana Beck, the violence arrived in their mountain community in northwest Montana in April when a friend texted them a picture of blood in the snow. A local bar owner had been shot to death. It was Flathead County’s 12th homicide since the start of 2020, one of the region’s most violent periods in recent memory.

“I was floored that something like that happened a mile down the road,” said Ms. Beck.

Violent crime isn’t just rising in the nation’s cities. Murder rates across the rural U.S. have soared during the pandemic, data show, bringing the kind of extreme violence long associated with major metropolises to America’s smallest communities.

Homicide rates in rural America rose 25% in 2020, according to the Centers for Disease Control and Prevention. It was the largest rural increase since the agency began tracking such data in 1999. The CDC considers counties rural if they are located outside metropolitan areas defined by the federal government.

The rise came close to the 30% spike in homicide rates in metropolitan areas in 2020.

The CDC hasn’t analyzed 2021 homicide data yet. In some rural counties, murder rates remained high last year, while in others they have begun to recede along with Covid, data from local law-enforcement agencies shows.

County sheriffs are trying to hire more deputies. Small-town prosecutors, unaccustomed to handling numerous homicides cases, find themselves overwhelmed with them.

In cities, law enforcement and civic leaders have blamed the increase in violent crime on factors such as police pulling back after racial-justice protests, the proliferation of guns, initiatives to release more criminal suspects without bail and a pandemic pause in gang-violence prevention programs.

In rural counties, where ties between police and locals are often less fraught, officials say the reasons for the rising violence are hard to pinpoint. They speculate that the breakdown of deeply rooted social connections that bind together many small communities, coupled with the stress of the pandemic, played a role.

Pastors point to the suspension of rituals such as in-person church services, town gatherings and everyday exchanges between neighbors. Such interactions can serve as guardrails, helping to prevent conflicts from turning violent. The psychological and financial stress due to isolation and job loss were especially pronounced in remote areas, where social services were limited even before Covid-19 struck, local leaders say.

As the pandemic took hold in the spring of 2020, fights between family members, acquaintances and even strangers escalated more frequently into deadly confrontations, authorities in some rural counties said.

“You saw the shutdown, crime started increasing, people were on edge, mental-health problems increased, more alcohol was sold,” said Brian Heino, sheriff of Flathead County in Montana.

An American Farm Bureau Federation poll taken in December 2020 found that two-thirds of adults in rural America between the ages of 18 and 44 said they experienced more mental-health challenges than in the prior year. According to a CDC survey conducted in June 2020, 12% of responding adults in rural areas reported starting or increasing their use of drugs or alcohol to cope with pandemic-related stress.

The homicides have been challenging for law enforcement to address. Small numbers of deputies must patrol expansive geographic areas, and the killings were often not connected to each other, making it more difficult to target particular criminal groups or devise effective prevention strategies.

“It’s not like you got gang members dealing drugs, and you can get boots on the ground, and undercover buys and prosecutions,” Marion County’s Sheriff Wallace said. “These are really tough to stop.”

Veteran law-enforcement officials said they had never before witnessed the level of violence of the past two years.

“It was like people lost their ever-lovin’ minds,” said Ms. McCoy, the prosecuting attorney in White County, a dry county in central Arkansas with poultry farms and a Christian university.

In Marion County, S.C., which has about 28,700 residents and more than 100 churches, pews used to be packed every Sunday. After services, people would pour into restaurants to eat seafood and Southern cooking.

When Covid struck, some churches stopped in-person services, and restaurants shut down. Walmart started closing early. People no longer stopped to socialize in the grocery store.

Authorities in rural counties said that as the pandemic wore on, troubled individuals became even more isolated, and in some cases, tipped over the edge.

“There are parts of our county that don’t even have internet service,” said Tammy Erwin, a victims’ advocate for the Marion County Sheriff’s Office. “So it’s not like everyone could just jump on Zoom.”

Just before midnight on Oct. 28, 2020, one of Sheriff Wallace’s deputies came upon an SUV with its windows fogged outside the Park & Blow Handi Mart. He could make out a man inside sweating heavily and sitting on something. When he looked closer, he saw the body of a woman, eyes bulging. She had been stabbed multiple times with an ice pick.

Johnny Michael Dillard, 57, was charged with killing Kemethia Spain, 51, whom he had known for a few years. Several people came forward to say Mr. Dillard had talked openly about wanting to know what it was like to kill someone. He was found not competent to stand trial and has been committed to a facility for the mentally ill.

Ms. Spain’s homicide was one of 15 that Marion County deputies responded to over the past two years, equal to the previous 10 years combined. “I’ve never seen these numbers before, nothing even really close to that,” said Sheriff Wallace.

As lockdowns took hold, the sequestering of family members inflamed household tensions. In Montana, domestic-violence incidents reached a record high in 2020, then jumped even higher in 2021, according to state crime data.

In June 2020, Josh Buls, a detective in the sheriff’s office in Flathead County, arrived at a home in Olney to find the bodies of a woman, her 3-year-old daughter and a male friend. All had been stabbed to death. The perpetrator, the woman’s estranged husband, had shot himself in the head before deputies arrived.

The killings shocked not only the 146 residents of the mountain town, but many in the sheriff’s office, which patrols more than 5,000 square miles of rugged territory bordering Canada. One deputy who responded to the 911 call that day decided to leave the profession, said Mr. Buls.

The six homicides in 2020 in this county of 108,000 were a record, as were the 51 statewide, according to state crime analysts.

Flathead County’s Sheriff Heino said rising methamphetamine use and mental-health problems during the pandemic contributed to more erratic behavior and more violent interactions between officers and suspects. Law enforcement in the county was involved in six shootings since May 2020, including four fatal ones. Before then, the last time a Flathead County deputy had killed anyone was 2007.

When the owner of Snow Slip Motel Bar & Cafe near Essex, Mont., was shot dead in April, news spread quickly among those living near the peaks and forests of Glacier National Park. Jim Noffsinger, a 57-year-old construction superintendent and rancher, locked his front doors for the first time.

“I’ve been accused of many things in my life, but being scared was never one of them,” he said. “Such a place of serenity has taken on a dark shadow.”

For Ms. McCoy, the prosecutor from White County, Ark., the unprecedented string of homicides in 2020 began the same month that Covid hit. Over a nightmarish 10 weeks starting that March 4, six people were murdered in the small towns that dot the county of 77,000, about 60 miles northeast of Little Rock.

Ms. McCoy searched for any common thread. Three of the victims were men in their 70s who lived alone, including one who was shot in the face when he opened his front door.

“At first we’re thinking, wow, if you’re an older man living by yourself, you might want to move out of county, because apparently it’s not safe in White County for you,” said Ms. McCoy.

To keep the cases straight, Ms. McCoy took out a white board she hadn’t used in years. As the killings continued, her “murder board” filled up, and she finally had to rewrite the names of victims and suspects and hearing dates in smaller letters to fit. It was the deadliest stretch of her 23-year career.

She said she felt helpless. “I don’t know what to say, besides y’all stop killing each other,” said Ms. McCoy.

With homicide cases mounting, Ms. McCoy told her staff of prosecutors who had little experience with murder cases to focus on their families when the workday ended. For the first time in her career, she said, she had trouble clearing her mind of gruesome crime-scene images when she returned home to her own family each night.

During an interview in her office in Searcy, Ark., as a rescue cat named Catticus maneuvered between murder case files stacked on the floor, Ms. McCoy shed tears recalling the day in June 2020 when she and county detectives arrived at a house in the town of Bald Knob to find the body of a 5-year-old girl whose throat had been slit by her father. They found the father overdosed in his car in a nearby state park a few hours later. Attempts to revive him failed.

Prosecutors in rural areas have struggled to identify motives behind many of the murders. Ms. McCoy concluded that the stress of the pandemic had contributed to disagreements escalating into violence.

That spring, 77-year-old Julius Williams Jr. was killed in what appears to have been a dispute about his lawn. Mr. Williams had lived much of his life in Kensett, a town of about 1,500 with weathered houses, weedy yards and a single main intersection.

Jason Baxter, a 26-year-old resident of a nearby trailer park, asked Mr. Williams if he could mow the older man’s yard, according to court records. Mr. Williams declined.

Days later, police found him lying inside his front door in a pool of blood. Mr. Baxter admitted to police that he had shot Mr. Williams, who was Black, “right in the eye,” according to a transcript of his police interview.

Investigators found rambling notes written by Mr. Baxter, who is white, about protecting white children.

Lee Short, a lawyer appointed by the state to represent three White County murder suspects, including Mr. Baxter, said Mr. Baxter had mental-health issues.

Mr. Williams’s daughter Thunder Nkere said she couldn’t believe he was killed in the tiny town where she grew up. “It’s one of those little country bumpkin places where everyone knows each other,” she said.

Last year, the number of homicides in White County declined to four. Ms. McCoy said she hoped the easing of the pandemic would help continue the decrease in numbers.

Killings in the county haven’t yet fallen to prepandemic levels, and the sheriff’s office has strained to keep up, even after more than doubling the number of detectives. It recently converted a donated ambulance into a crime-scene vehicle. It also bought new equipment, including a machine that can extract cellphone data in hours, compared with waiting months for a state lab to process such data.

In April, Mr. Baxter pleaded guilty to first-degree murder and was sentenced to 40 years in prison. Ms. McCoy wiped away one line on her murder board.

Violent crime isn’t just rising in the nation’s cities. Murder rates across the rural U.S. have soared during the pandemic, data show, bringing the kind of extreme violence long associated with major metropolises to America’s smallest communities. Homicide rates in rural America rose 25% in 2020, according to the Centers for Disease Control and Prevention. It was the largest rural increase since the agency began tracking such data in 1999. The CDC considers counties rural if they are located outside metropolitan areas defined by the federal government. The rise came close to the 30% spike in homicide rates in metropolitan areas in 2020. The CDC hasn’t analyzed 2021 homicide data yet. In some rural counties, murder rates remained high last year, while in others they have begun to recede along with Covid, data from local law-enforcement agencies shows. Veteran law-enforcement officials said they had never before witnessed the level of violence of the past two years. “It was like people lost their ever-lovin’ minds,” said Ms. McCoy, the prosecuting attorney in White County, a dry county in central Arkansas with poultry farms and a Christian university. For Ms. McCoy the unprecedented string of homicides in 2020 began the same month that Covid hit. Over a nightmarish 10 weeks starting that March 4, six people were murdered in the small towns that dot the county of 77,000, about 60 miles northeast of Little Rock.

The children of first-generation immigrants growing up close to the bottom of the income distribution (say, at the 25th percentile) are more likely to reach the middle of the income distribution than are children of similarly poor U.S.-born parents. no matter which country their parents came from, children of immigrants are more likely than the children of the U.S.-born to surpass their parents’ incomes when they are adults. To conduct our analysis, we needed data that links children to parents. For the historical data, we used historical census records to link sons living in their childhood homes to census data collected 30 years later when these young men had jobs of their own. The first striking takeaway is that, as a group, children of immigrants achieve more upward mobility than the children of U.S.-born fathers. We focus on the children of white U.S.-born fathers because the children of Black fathers tend to have lower rates of upward mobility. So, the mobility advantage that we observe for the children of immigrants would be even larger if we compared this group to the full population.The second notable takeaway is that even children of parents from very poor countries like Nigeria and Laos outperform the children of the U.S.-born raised in similar households. The children of immigrants from Central American countries—countries like Guatemala, El Salvador and Nicaragua that are often demonized for contributing to the “crisis” at the southern border—move up faster than the children of the U.S.-born, landing in the middle of the pack (right next to children of immigrants from Canada).Our third finding is that the mobility advantage of the children of immigrants is just as strong today as it was in the past. What’s more, some of the immigrant groups that politicians accused long ago of having little to contribute to the economy—the Irish, Italians, and Portuguese—actually achieved the highest rates of upward mobility. For the past, we are only able to study sons because we cannot link daughters who change their name at marriage. But in the modern data we can see that this pattern applies to daughters as well.

Ran Abramitzky and Leah Boustan, "Why the Children of Immigrants Are the Ones Getting Ahead in America,"Time Magazine, June 1, 2022, https://time.com/6182715/immigrants-children-us-mobility/

Why the Children of Immigrants Are the Ones Getting Ahead in America

In April 2020, the New York Times ran a special feature called “I Am the Portrait of Downward Mobility.” “It used to be a given that each American generation would do better than the last,” the piece began, “but social mobility has been slowing over time.”

In paging through the profiles, we couldn’t help noticing one group of Americans who defies this trend: the children of immigrants. Sonya Poe was born in a suburb of Dallas, Texas to parents who immigrated from Mexico. “My dad worked for a hotel,” Sonya recalled. “Their goal for us was always: Go to school, go to college, so that you can get a job that doesn’t require you to work late at night, so that you can choose what you get to do and take care of your family. We’re fortunate to be able to do that.”

The dream that propels many immigrants to America’s shores is the possibility of offering a better future for their children. Using millions of records of immigrant families from 1880 to 1940 and then again from 1980 to today, we find that the in past and still today children of immigrants surpass their parents and move up the economic ladder. If this is the American Dream, then immigrants achieve it—big time.

One pattern that is particularly striking in the data is that the children of immigrants raised in households earning below the median income make substantial progress by the time they reach adulthood, both for the Ellis Island generation a century ago and for immigrants today. The children of first-generation immigrants growing up close to the bottom of the income distribution (say, at the 25th percentile) are more likely to reach the middle of the income distribution than are children of similarly poor U.S.-born parents.

What’s more, no matter which country their parents came from, children of immigrants are more likely than the children of the U.S.-born to surpass their parents’ incomes when they are adults. This pattern holds both in the past and today, despite major changes in U.S. immigration policy over the past century, from a regime of nearly open borders for European immigrants in 1900 to one of substantial restrictions in recent decades. Children of immigrants from Mexico and the Dominican Republic today are just as likely to move up from their parents’ circumstances as were children of poor Swedes and Finns a hundred years ago.

Not only does upward mobility define the horizons of people’s lives, but it also has implications for the economy as a whole. Even immigrants who come to the U.S. with few resources or skills bring an asset that is hugely beneficial to the U.S. economy: their children. The rapid success of immigrants’ children more than pays for the debts of their parents.

To conduct our analysis, we needed data that links children to parents. For the historical data, we used historical census records to link sons living in their childhood homes to census data collected 30 years later when these young men had jobs of their own.

Think of us like curious grandchildren searching branches of their family tree online, but a million times over. We started by digging through websites like Ancestry.com that allow the public to search for their relatives. From here, we developed methods to automate these searches so we could follow millions of immigrants and their children in the records.

Our modern data is based on federal income tax records instead. The tax records allow researchers to link children to their parents as tax dependents, and then observe these children in the tax data as adults.

When we compiled this data, what do we see?

The first striking takeaway is that, as a group, children of immigrants achieve more upward mobility than the children of U.S.-born fathers. We focus on the children of white U.S.-born fathers because the children of Black fathers tend to have lower rates of upward mobility. So, the mobility advantage that we observe for the children of immigrants would be even larger if we compared this group to the full population.

The second notable takeaway is that even children of parents from very poor countries like Nigeria and Laos outperform the children of the U.S.-born raised in similar households. The children of immigrants from Central American countries—countries like Guatemala, El Salvador and Nicaragua that are often demonized for contributing to the “crisis” at the southern border—move up faster than the children of the U.S.-born, landing in the middle of the pack (right next to children of immigrants from Canada).

Our third finding is that the mobility advantage of the children of immigrants is just as strong today as it was in the past. What’s more, some of the immigrant groups that politicians accused long ago of having little to contribute to the economy—the Irish, Italians, and Portuguese—actually achieved the highest rates of upward mobility. For the past, we are only able to study sons because we cannot link daughters who change their name at marriage. But in the modern data we can see that this pattern applies to daughters as well.

Today, we might not be that surprised to learn that the children of past European immigrants succeeded. We are used to seeing the descendants of poor European immigrants rise to become members of the business and cultural elite. Many prominent leaders, including politicians like President Biden, regularly emphasize pride in their Irish or Italian heritage. But, at the time, these groups were considered the poorest of the poor. In their flight from famine, Irish immigrants are not too dissimilar from immigrants who flee hurricanes, earthquakes, and violent uprisings today.

We often hear concerns about how poor immigrants will fare and whether their children will get trapped in low-paying jobs or dependent on government support. But our data sleuthing should lay these fears to rest. The children of immigrants do typically make it in America. And it most often takes them only one generation to rise up from poverty.

One question that arises with our work is: what about children who arrive without papers? Undocumented children face more barriers to mobility than other children of immigrants. Fortunately, this group is relatively small even in recent years: only 1.5 million (or five percent) of the 32 million children of immigrant parents are undocumented today. Indeed, this number is small because many children of undocumented immigrants are born in the U.S. and thus are granted citizenship at birth.

The children in our data from countries like Mexico and El Salvador are those whose parents benefited from an earlier legalization effort in the mid-1980s. They are doing remarkably well now, and we believe that their counterparts today have this potential, as well. Children who arrive in the U.S. without papers face barriers to mobility—and not because they put in any less effort, but because they encounter obstacles all along their path. With a stroke of a pen, politicians can make that happen but, so far, this legislation has remained out of reach.

What enables the children of immigrants to escape poor circumstances and move up the economic ladder? The answer we hear most often is that immigrants have a better work ethic than the US-born and that immigrant parents put more emphasis on education.

We agree that the special features of immigrant families could be part of the story (although it’s hard to tell in our data). Yet when we crunched the numbers we found something surprising: immigrants tend to move to those locations in the U.S. that offer the best opportunities for upward mobility for their kids, whereas the U.S.-born are more rooted in place.

Generations of social science research has confirmed that where children grow up influences their opportunities in life. We find that immigrant parents are more likely than U.S.-born parents to settle in these high-opportunity areas, which are flush with good jobs and offer better prospects for mobility in the next generation. As striking proof that geography matters, we see that children of immigrants out-earn other children in a broad national comparison, but they do not earn more than other children who grew up in the same area. In terms of economic fortunes, the grown children of immigrants look similar to the children of U.S.-born parents who were raised down the block, or in the same town. This pattern implies that the primary difference between immigrant families and the families of the U.S.-born is in where they choose to live.

One implication of our findings is that it is very likely that U.S.-born families would have achieved the same success had they moved to such high-opportunity places themselves. In fact, we find that the children of U.S.-born parents who moved from one state to another have higher upward mobility than those who stayed put: their level of upward mobility is closer to (but not quite as high as) that of the children of immigrants who moved from abroad. So, you might ask: why don’t US-born families move out of a region when job opportunities dwindle?

Ironically, J.D. Vance (who is now running for Senate in Ohio on an anti-immigration platform) poses this question in his bestseller Hillbilly Elegy,aboutgrowing up in Middletown, Ohio, only 45 minutes from the border with Kentucky, the state where his family had lived for generations. For Vance, moving up the ladder meant moving out of his childhood community, a step that many Americans are unwilling to take. He went on to enlist in the Marines, and then to Ohio State and Yale Law School—“Though we sing the praises of social mobility,” he writes, “it has its downsides. The term necessarily implies a sort of movement—to a theoretically better life, yes, but also away from something.”

Vance is hitting on the cost of attaining upward mobility for children of U.S.-born parents. Many of the children of U.S.-born parents grow up in areas where their families settled long before, so economic mobility for them is often coupled with the costs of leaving home. By contrast, immigrants already took the step of leaving home to move to America, so they may be more willing to go wherever it takes within the country to find opportunity. In other words, U.S.-born families are more rooted in place, while immigrant families are more footloose—and this willingness to move toward opportunity seems to make all the difference.

Rising earnings inequality is dominated by rising between-firm inequality. Our analysis as well as the recent literature emphasizes that this largely reflects how firms are organizing themselves in terms of their workforce. High (low) earnings workers are more likely to work with each other (increased segregation), and high (low) earnings workers are more likely to work at high (low) firm premia firms (sorting). Our contribution is to highlight the dominant role of industry effects in accounting for this structural change of how firms organize their workforces. Most of rising between-firm inequality is accounted for by rising between-industry dispersion in earnings. The between-industry component accounts for 61.9% of total increasing earnings inequality, and 72.8% of between-firm inequality growth. This changes the narrative of the sorting and segregation contributions. High (low) earnings workers are more likely to work with each other in specific industries and high (low) earnings workers are more likely to work in high (low) average firm premia industries. Not only do industry effects dominate but it is a relatively small share of industries that account for virtually all the increasing dispersion in earnings across industries. We find that about ten percent of the 301 detailed 4-digit NAICS industries account for almost 100% of the rising between-industry dispersion, while accounting for less than 40% of employment. The ten percent of industries that account for virtually all of the increase are drawn from the top and bottom of the earnings distribution in terms of industry-level averages. For those industries at the top of the earnings distribution, their contribution is dominated by rising inter-industry earnings differentials. For industries at the bottom of the earnings distribution, their contribution is dominated by shifts in employment to these very low earnings industries. For both sets of industries at the top and the bottom of the earnings distribution, increased sorting and segregation between industries dominates but increased dispersion in between industry firm premia also plays an important supporting role. Increased sorting is relatively more important for the rising between-industry dispersion from the industries at the bottom of the earnings distribution. In contrast, increased segregation is relatively more important for the rising between industry dispersion from the industries at the top of the earnings distribution. The dominance of industry effects is closely linked to the rising importance of mega (10,000+) firms in the U.S. economy. The increasing share of employment accounted for by mega firms is concentrated in the thirty 4-digit industries that account for virtually all of rising between-industry dispersion. This rising share of employment at mega firms is accompanied by a declining size-earnings premium in the eleven low-paying industries. For mega firms in the nineteen high-paying industries in the top 30, earnings premia rise sharply relative to other industries (albeit not as rapidly as other large but not mega firms in these industries). Our findings imply that understanding rising earnings inequality during the last several decades requires understanding the restructuring of how firms organize themselves in a relatively small set of industries. Moreover, since it is the between-industry contribution that dominates, it is the common effects of re-organization across firms in the same industry that matter.Many mechanisms such as changing technology, market structure, and globalization likely underlie rising earnings inequality. The focus of future research on the impact of such changes on rising earnings inequality should be on the uneven and concentrated impact of such mechanisms across industries. The top ten percent of industries that account for virtually all of rising between-industry inequality are not randomly spread across the distribution of industries but concentrated in specific industry clusters in the tails of the earnings distribution. At the high end, dominant industries are drawn from high-tech and STEM intensive industries, finance, mining, and selected industries in health. At the low end, dominant industries are drawn from selected industries in retail and health. Notably absent are the vast majority of industries in manufacturing. The top thirty industries are in industry clusters that have exhibited structural transformations that have been the subject of independent study. Our findings imply that the role of inter-industry earnings differentials and the changing composition of employment across industries is much more important for understanding earnings inequality than suggested by the recent literature.

“….We provide detail about these thirty industries in Table 3 (the industries in Table 3 are sorted by NAICS). The largest contribution is from Restaurants and Other Eating Places (7225), which alone accounts for 16.9% of between-industry variance growth. The second-largest contribution occurs among Other General Merchandise Stores (4529), which accounts for 6.8%. While the most important two industries to increasing inequality tend to offer low-paying jobs, the other three industries that account for more than 5% of between-industry variance growth are high-paying: Software Publishers (5112), Computer Systems Design (5415), and Management of Companies (5511). What about the other 271 4-digit NAICS industries? The contributions of these industries to between-industry variance growth are summarized in Table 2. There are 145 industries that each contribute approximately 0.0% (to be precise, greater than −0.05% and less than 0.05%) to between industry variance growth. This says that almost half of all 4-digit NAICS industries contribute essentially nothing to inequality growth. There are 71 industries that contribute between 0.05% and 1.0%, accounting for 22.3% of between-industry variance growth. These industries are basically offset by another 55 industries that have a negative contribution (< −0.05%), accounting for −20.3% of between-industry variance growth. As seen in Table 4, the top thirty industries include nineteen high-paying industries that account for 54.1% of between-industry variance growth, and eleven low-paying industries that account for 44.1% of between-industry variance growth. The other 271 industries that have small contributing and offsetting contributions to increasing inequality do not occur systematically among high-paying vs. low-paying industries. 146 high-paying industries account for 1.3% of between-industry variance growth, and 125 low-paying industries account for only 0.6% of between-industry variance growth….”

In a new paperwe document an important determinant of how much an employer pays: the industry. An employer’s industry is determined by its production processes or its products. Pay differentials across industries are large and have widened over time, leading to increases in labour earnings inequality. For example, restaurants tend to be low-paying employers. Our data indicate that in recent decades in the US, about one-tenth of the rise in labour earnings inequality can be attributed to restaurants employing more people and paying them less. We use matched employer-employee data for 18 US states to study labour earnings inequality (hereafter, simply ‘inequality’; note that this excludes self-employment and capital income). Thirty industries (out of a total of 301) can account for nearly all of the rise in inequality from the late 1990s to the late 2010s. In these 30 industries, ‘mega firms’ (which employ 10,000 or more) have seen a massive surge in employment. This column describes some of our main findings. Industries drive increasing inequality. Figure 1 reports our measure of inequality, which is the variance of log (annual) labour earnings. The contributions of different industries to between-industry inequality are shown in Figure 2. Recall from Figure 1 that between-industry inequality (the green region of Figure 1) accounts for 61.9% of the total increase in inequality. The 30 industries that contribute most to increasing inequality explain nearly all (98.1%) of the between-industry contribution to inequality - despite employing less than 40% of the workforce. Of these 30 industries, 19 are high-paying and account for 54.0% of the increase in between-industry inequality; 11 are low-paying and account for 44.1% of this increase. The other 271 industries account for only 1.9% of rising inequality. Is increasing inequality driven by changes in employment or earnings? Figure 3 shows that there are sizeable differences in the answer to this question depending on whether the industry is high- versus low-paying. Among high-paying industries, 83.9% of the contribution to inequality is due to strong increases in earnings, while any employment increases in these industries only account for only 16.1% of this rise. In contrast, the employment among the 11 most important low-paying industries surged, accounting for 68.3% of their contribution to between-industry inequality. A much more modest decline in earnings in these low-paying industries explains the remaining 31.7%. Industries drive recent changes in inequality in the US. Thirty out of the 301 industries in our classification can explain nearly all of the rise in between-industry inequality. Continued study of these industries can help shed light on the mechanisms by which inequality continues to increase.

This note explores the substitutability between policy rate hikes and reductions in the size of the Federal Reserve's balance sheet for the removal of policy accommodation. We do so using a version of the FRB/US model augmented to incorporate the effects of changes in the Federal Reserve's asset holdings on term premiums. We illustrate how the equivalence between policy rate hikes and balance sheet reductions in the model depends on assumptions about the evolution of the balance sheet over an extended period. Overall, the model predicts that reducing the size of the balance sheet by about $2.5 trillion over the next few years, as opposed to maintaining the size at its peak level, would be roughly equivalent to raising the policy rate a little more than 50 basis points on a sustained basis. However, this estimate is associated with considerable uncertainty. Table 1 reports some key simulation statistics for 2024:Q3, an illustrative date that corresponds to the resumption of asset purchases under the full-runoff scenario. For the full-runoff scenario, total asset holdings in 2024:Q3 are roughly $0.8 trillion (2.8 percent of GDP) lower than for the baseline. The macroeconomic effects of this smaller balance sheet are offset through a small reduction in the level of the federal funds rate of only 9 basis points, reflecting the modest difference in term premiums between the two scenarios. In other words, the differences in interest rates and economic effects of allowing full runoff of the balance sheet as opposed to the capped runoff in the FOMC's plans are, by this calculation, very small. For the no-runoff scenario, the level of assets is $2.1 trillion (7.7 percent of GDP) higher in 2024:Q3 than in the baseline balance sheet scenario, a difference that widens to about 2.5 trillion by the time the balance sheet size under the baseline scenario reaches its trough in mid-2025; the policy rate is 56 basis points higher in our reference quarter. Put differently, the balance sheet reduction in the baseline scenario is equivalent to a bit more than two sustained 1/4 percentage-point hikes in the policy rate when measured against the no-runoff scenario

Edmund Crawley, Etienne Gagnon, James Hebden, and James Trevino, "Substitutability between Balance Sheet Reductions and Policy Rate Hikes: Some Illustrations and a Discussion,"Federal Reserve Board, June 3, 2022, https://www.federalreserve.gov/econres/notes/feds-notes/substitutability-between-balance-sheet-reductions-and-policy-rate-hikes-some-illustrations-20220603.htm

Substitutability between Balance Sheet Reductions and Policy Rate Hikes: Some Illustrations and a Discussion

This note explores the substitutability between policy rate hikes and reductions in the size of the Federal Reserve's balance sheet for the removal of policy accommodation. We do so using a version of the FRB/US model augmented to incorporate the effects of changes in the Federal Reserve's asset holdings on term premiums. We illustrate how the equivalence between policy rate hikes and balance sheet reductions in the model depends on assumptions about the evolution of the balance sheet over an extended period. Overall, the model predicts that reducing the size of the balance sheet by about $2.5 trillion over the next few years, as opposed to maintaining the size at its peak level, would be roughly equivalent to raising the policy rate a little more than 50 basis points on a sustained basis. However, this estimate is associated with considerable uncertainty.

Economic context

In response to the pandemic-driven recession, the FOMC lowered the target range for the federal funds rate rapidly to its effective lower bound (ELB) of between 0 and 0.25 percent. The Committee also purchased Treasury securities and agency mortgage-backed securities (MBS) to support smooth market functioning and progress toward its maximum employment and price stability goals. At the conclusion of the asset purchase program, the Federal Reserve's balance sheet size had increased by roughly $4.8 trillion (20 percent of annual GDP).

Policymakers have since turned to removing policy accommodation. In March 2022, they started raising the target range for the federal funds rate and conveyed their anticipation that ongoing increases in the policy rate would be appropriate at future meetings. In May 2022, the FOMC released its Plans for Reducing the Size of the Federal Reserve's Balance Sheet, with reductions beginning on June 1 of this year.2 Both the policy rate and balance sheet tools are expected to contribute to a firming of the stance of monetary policy.

The relationship between balance sheet reductions and policy rate hikes

In the model we use, the policy rate and balance sheet tools influence the economy primarily through their effects on the medium- and longer-term interest rates that drive economic activity.3 Policy rate actions and communications do so by affecting the cost of short-term borrowing and expectations about the path of short-term interest rates. Balance sheet policies primarily influence the term premiums embedded in medium- to longer-term yields by changing the supply—current and expected—of longer-term securities held by the public. In the model, the two tools are substitutes in terms of their ability to affect medium- and longer-term interest rates, employment, and inflation when the ELB constraint is not binding. Because of this substitutability, the model can be used to translate balance sheet reductions in terms of equivalent increases in the path of the federal funds rate that would lead to similar macroeconomic outcomes.

The model implies that a one-time permanent reduction in the Federal Reserve's holdings of 10-year equivalent Treasury securities equal to 1 percent of nominal GDP raises the term premium on a 10-year Treasury security by about 10 basis points, all else equal.4,5 In the model, this amount of policy tightening can also be achieved by raising the average expected path of the federal funds rate over the coming decade by about 10 basis points. Together, these relationships provide a simple rule of thumb for the substitutability between balance sheet reductions and policy rate hikes in the model when the economy is away from the ELB. However, as we discuss next, the translation of dollar amounts of balance sheet reductions into equivalent policy rate hikes depends on the evolution of the size and maturity composition of the balance sheet.

Illustrative balance sheet scenarios

We consider three illustrative balance sheet scenarios:

Baseline: Shrink the balance sheet starting in June 2022 in a manner consistent with the FOMC's Plans for Reducing the Size of the Federal Reserve's Balance Sheet, as captured by the illustrative baseline balance sheet scenario featured in the 2021 SOMA annual report.6 After a three-month phase-in period, let up to $60 billion in Treasury securities and up to $35 billion in agency MBS run off the balance sheet per month.
No runoff: Keep the balance sheet size constant in dollar terms by rolling over Treasury securities and agency MBS until growth in the demand for Federal Reserve liabilities provides a basis for resuming organic growth of the balance sheet.
Full runoff: Shrink the balance sheet through the full runoff of all Treasury securities as well as maturing and prepaid agency MBS starting in June 2022, without any phase-in period (about $105 billion per month, on average over the runoff period).

The top-left and top-right panels of figure 1 report the total value of assets held by the Federal Reserve in current dollar terms and as a share of nominal GDP, respectively, in the scenarios. In all three scenarios, it is assumed that the ratio of reserves to nominal GDP declines to the average level of reserves held in December 2019 as a share of nominal GDP. Net asset purchases then resume to maintain the ratio at this illustrative value and grow other Federal Reserve liabilities in line with nominal GDP, consistent with a ratio of total assets to nominal GDP of about 21 percent. The no-runoff scenario provides a counterfactual benchmark to assess the overall effect of balance sheet reductions in the other two scenarios.

We built genome-wide polygenic scores for heritable traits, including height, body mass index, lipoprotein concentrations, cardiovascular disease, and intelligence, using summary statistics of genome-wide association studies in Europeans. Subsequently, we applied these scores to the genomes of ancient European populations. Our results revealed that after the Neolithic, European populations experienced an increase in height and intelligence scores, decreased their skin pigmentation, while the risk for coronary artery disease increased through a genetic trajectory favoring low HDLconcentrations. These results are a reflection of the continuous evolutionary processes in humans and highlight the impact that the Neolithic revolution had on our lifestyle and health.We used ancient DNA (aDNA) data of 827 ancient individuals from Western Eurasia, which were publicly available and aggregated into a database released 22 February 2019, by the David Reich lab (version 37.2) The aDNA data was used in conjunction with DNA data of 250 randomly selected modern individuals from Western Europe, which were downloaded from 1000G database Using GWAS summary statistics from GWAS catalogue and United Kingdom Biobank, which can be seen in Supplementary Table S1, we calculated PRS for each individual for various traits respectively, using a QTL p-value threshold of 10-6, in 250 kb windows. Multiple thresholds were further tested for assuring the robustness of the findings. We supplemented the effect of missing variants in a sample with the average of the entire population. We then scaled the PRS distribution to have a range of 1 to −1 before correlating them with the carbon-dated sample age. One general observation is the existence of a clear difference in the trajectories of the various traits before and after the Neolithic revolution: few changes are seen between the Early Upper Paleolithic until the Neolithic period, with a general acceleration of the evolutionary processes thereafter. Interestingly, while the period between the Early Upper Paleolithic and the Neolithic is characterized by stagnation or slight decrease in PRS related to intelligence, the genetic data show a clear increase in the scores for educational attainment, intelligence, and fluid intelligence from the Neolithic onwards, while the traits related with unipolar depression tend to decrease from that era on (Figures 3A,B). The most significant differences can be observed comparing the pre-Neolithic and Neolithic groups, as well as the post-Neolithic and modern groups, whereas the period between the Neolithic and post-Neolithic shows a very constant distribution of PRS scores (Figure 3C).In conclusion, using genetic models applied to ancient genomes, we describe important changes that have taken place in the physiological traits of modern Homo sapiens populations living in Europe since the Early Upper Paleolithic. Although the exact extent of the evolutionary changes Europeans underwent remain unclear, based on these genetic trajectories we can see that while the first 40,000 years in Europe represented a period of relative stagnation in the evolution of these traits, the spread of Neolithic populations and of the cultural complex associated with agriculture and animal domestication has led to important changes in height, weight, skin color, an increase in intellectual attainment, as well as in cholesterol metabolism and the risk for cardiovascular diseases. These new insights put into light the processes that have shaped human physiology since the migration of modern humans throughout Europe.

Yunus Kuijpers, Jorge Domínguez-Andrés, Olivier Bakker, Manoj Kumar Gupta, Martin Grasshoff, Cheng-Jian Xu, Leo A.B. Joosten, Jaume Bertranpetit, Mihai G. Netea and Yang Li, “Evolutionary Trajectories Of Complex Traits In European Populations Of Modern Humans,” Frontiers in Genetics,March 28, 2022, https://www.frontiersin.org/articles/10.3389/fgene.2022.833190/full

This paper synthesizes what we have learned about human capital into four stylized facts. Fact 1: Human capital explains a substantial share of the variation in labor earnings within and across countriesThe bottom line is that naïve cross-sectional comparisons and studies with strong quasi-experimental research designs yield very similar estimates of the economic return to education. Overall, studies that identify returns to education using instrumental variables, regression discontinuity, and other quasi- experimental approaches yield estimates of an additional year of education ranging between 6 and 18 percent, with a median in the 10-12 percent range. This is slightly higher than the 10 percent return from a “naïve” Mincer model, most likely because of some combination of measurement error and higher returns for marginal students (Card 1999). Across all OECD countries, the median earnings premium for a four-year college / tertiary education is 52 percent, or roughly 13 percent per year of education Table 1 uses data from the 1979 National Longitudinal Survey of Youth (NLSY79), which tracks a cohort of youth ages 14 to 22 in 1979 as they progress through the labor market. To estimate returns to education over the life-course, I compute the average inflation-adjusted hourly wage for individuals between the ages of 25 and 54 over multiple observations, and then regress log average hourly wages on years of education, race and gender indicators, and cognitive ability as measured by adolescent scores on the Armed Forces Qualifying Test (AFQT). Column 1 shows that the average return to a year of education over an individual’s prime working years is 10.9 percent. The R-squared of this regression is 30 percent. Controlling for AFQT to account for “ability bias” reduces the coefficient on years of education to 7.2 percent and increases the R-squared of the regression to 35 percent. Column 3 shows the average return for different levels of educational attainment, with less than high school as the left-out category. High school graduates and four-year college graduates earn an average of 13 percent and 48 percent higher wages than those with less than a high school education, respectively.These results are similar in magnitude to the quasi-experimental studies discussed above and to naïve cross-sectional estimates from other data sources. Basic measures of human capital such as education and cognitive ability can explain at least one-third of the variation in wages in a recent cohort of US workers. However, one-third is probably a lower bound for the impact of human capital on earnings, for three reasons. First, the calculation here does not include variation in education quality between workers with the same level of attainment. Quality adjustment is particularly important, because nearly all expansion of US postsecondary education over the last few decades has occurred within less-selective institutions. Carneiro and Lee (2011) estimate that the college premium would have grown an additional 30 percent between 1960 and 2000 if pre-college education quality were held constant for the marginal college graduate. Second, several studies find a larger role for human capital when it is measured in a way that includes education but also other attributes. For example, Smith et al. (2019) study the impact of owner death or retirement on private pass-through businesses and find that 75 percent of profits are attributable to the owner’s human capital, rather than physical or financial assets. Card et al. (2018) and Song et al. (2019) decompose the variance of earnings in matched employer-employee data and find that “worker effects” account for 40 percent of the variance in earnings in West Germany and 50 percent in the US respectively. Because worker effects are invariant to firm pay premia and occupational shifts by construction, we can reasonably consider them an estimate of workers’ human capital. I think the contribution of signaling is probably small, for two reasons. First, many studies find positive returns to education even when no degree or credential is earned. This is important because signaling theory requires employers to observe the signal, and most people don’t report years of education on a resume. For example, studies of compulsory schooling compare groups of students who all seek to drop out as soon as they can, but some are required to stay in school longer based on when they were born during a calendar year. Many of the youth staying in school for an extra year do not end up obtaining a high school degree at all - they drop out in 11th grade rather than 10th grade. Nonetheless, such studies show that additional education leads to gains in earnings.A school construction program in Indonesia studied by Duflo (2001) mostly worked by increasing primary school enrollment, not receipt of degrees—but still led to later gains in wages. Aryal, Bhuller and Lange (2022) cleverly exploit the differential observability of compulsory schooling laws across regions in Norway to separate returns to human capital from signaling, and find that human capital accounts for 70 percent of the private return to secondary school education. empirical support for signaling theory is scant. Clark and Martorell (2014) find no difference in earnings between high school students who barely pass or fail an exit exam, implying that there is no signaling value of a high school diploma.Some studies do find that the return to education decreases over time as employers learn workers’ true ability, which is a testable implication of the signaling model Yet a similar test of the employer learning model in a more recent cohort finds that the return to education does not diminish with experienceFact 2: Human capital investments have high economic returns throughout childhood and young adulthood. There is strong evidence supporting value of skill investments in early childhood.Perhaps best-known are two randomized evaluations of preschool interventions from the 1960s: the High/Scope Perry Preschool Project and the Carolina Abecedarian Project. These studies are from decades ago, involving small-scale and intensive interventions for highly disadvantaged families, and thus their results may not generalize to larger and more recent programs. However, several studies of more recent preschool interventions also find substantial impacts —although the evidence also provides a puzzle. Pre-K programs often provide only a short-term boost to test scores that fades out in a few years. Yet they have longer-run impacts on important life outcomes such as high school graduation and college attendance, as well as non-educational metrics like reductions in crime and teen pregnancy and improved health later in life (for example, Ludwig and Miller 2007, Deming 2009, Gray-Lobe, Pathak and Walters 2021). In addition, there are many other methods of early childhood investment: pre-natal care, early child health care, food and nutrition support, home visits to encourage practices like breast-feeding and smoking cessation, and others. In recent essays in this journal, Aizer, Hoynes, and Lleras-Muney (2022) describe the evidence of long-term benefits from policy interventions affecting low-income children like Medicaid and food stamps, while Wust (2022) presents the evidence from the Nordic countries about the benefits of universal provision of early childhood investments in pre-natal care, health care at time of birth, and early childhood health care. Again, any benefit-cost analysis of these programs needs to take a long-term view, because many of these benefits only become apparent later in life. In sum, the evidence suggests that human capital investments are, at least in rough terms, equally productive between the ages of 0 and 25. Fact 3: The technology for producing foundational skills such as numeracy and literacy is well- understood, and resources are the main constraint. The overall picture is that some specific input investments work very well, but many do not, and it is often hard to predict ahead of time. I interpret the evidence as follows. First, at least in the United States, increased school spending is productive on the margin. Increasing school spending from current levels would produce substantially more human capital and may even pay for itself in the long run. The technology for producing basic math and literacy skills in school-aged children is fairly well-understood. Smaller class sizes, better school facilities, and more instructional time all have reliable impacts on the development of foundational academic skills. The inputs with the best track record of effectiveness - high-dosage tutoring, extra instructional time, personalization and teaching to the right level - mostly deliver to students “more of the same”, rather than reinventing the learning process. Second, while sharpening incentives works in some contexts, achievement gains are often short-lived and there is not much evidence of long-run benefits. An important caveat is that resources plus incentives appear to be more effective than resources alone. Third, simply giving schools money - and allowing them to spend it flexibly - may be a more reliable way to increase human capital than pinning our collective hopes on any particular “silver bullet” approach that all schools would be required to follow.This makes education experts queasy, and rightfully so. In a perfect world, increases in resources are combined with transparency and accountability for results. Yet the evidence suggests that “helicopter drops” of money are spent well enough to be worth the investment, at least in developed countries and in schools with strong internal accountability. However, just because school spending is economically productive on the margin does not mean that the money is spent optimally. It can be simultaneously true that school spending is productive and that much of it is wasted. We can probably always do better, and so innovation and experimentation are critical for increasing the productivity of human capital investments. Fact 4: Higher-order skills such as problem-solving and teamwork are increasingly economically valuable, and the technology for producing them is not well understood

David Demings, “Four Facts About Human Capital,” National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30149