Macro Roundup

Excess savings is the rise in cumulative total assets that is attributable to an abnormal rise in personal saving. As an example, in April 2020 the personal saving rate hit a record 33% (compared to about 7% pre-pandemic), boosting excess savings. Today, the aggregate personal saving rate is 4.4%, drawing down excess savings. What’s now pulling the stock of excess savings down? It’s chiefly a rise in nominal spending, not a drop in personal income. By mid-2021 personal outlays had bounced back to their pre-pandemic trend and by early 2022 they jumped far above trend. As the personal saving rate shrank the cumulative stock of excess savings began to decline. So far, Americans have spent down approximately $150B of the $2.5T in extra aggregate savings that they accumulated in 2020 and 2021. Excess savings have decreased, driven by high-income households spending more (and being taxed on their income/capital gains).

Joseph Politano, "Americans are Spending Their Excess Savings,"Apricitas Economics, June 4, 2022, https://apricitas.substack.com/p/americans-are-spending-their-excess

Americans are Spending Their Excess Savings

Or, well, not exactly. The jump in savings are a mechanical result of how deficit-financed government transfers show up in the official National Income and Product Accounts. For the public sector to run a deficit the private sector must run a surplus; for the public sector to go into debt the private sector must be building up savings. A massive shock to the size of the public sector deficit will create a corresponding large shock to the size of private sector savings—hence why deficit-financed stimulus resulted in excess savings.

Neither of those points means that excess savings are fake—just that they’re more complicated than they initially appear. It also means that we have to be careful in analyzing how Americans are spending down those savings as the economy continues to reopen.

An Excess Penny Saved…

First, let’s clarify some points. Personal saving—in the National Income and Product Accounts—represents the sum total of aggregate personal income that is not spent on aggregate personal outlays. In other words, if you summed together all the wages, dividends, government transfers, etc of US households and subtracted out their consumption spending, taxes, interest expenses, etc you would arrive at personal saving. That leaves a lot of ways for personal saving to manifest—increased bank account balances, additional retirement contributions, cryptocurrency purchases, paying down the principal of mortgages or other household debt, and even home remodeling (technically categorized as “residential investment”). In other words, it is not like there is precisely an additional $2.5 trillion sitting in household bank accounts because the personal saving rate has increased by $2.5 trillion.

It’s also important to distinguish between the flow of saving and the stock of savings. The personal saving rate represents the share of income saved in a given time period (say, one month) while personal savings represent the total value of accumulated assets. Excess savings is the rise in cumulative total assets that is attributable to an abnormal rise in personal saving. As an example, in April 2020 the personal saving rate hit a record 33% (compared to about 7% pre-pandemic), boosting excess savings. Today, the aggregate personal saving rate is 4.4%, drawing down excess savings.

The biggest positive contributors to excess savings are obvious; increased government transfers and decreased personal consumption expenditures. Those categories more than offset the drop in labor and capital income that occurred in 2020 and 2021. But again remember that for the public sector to go into debt the private sector must be building up savings; a positive contribution from government transfers only indicates that deficit financed spending was directed at households. Also, it would be hard for the contribution from government transfers to decrease significantly as definitionally public spending is classified as transfers and public revenue is separately classified as taxes.

Still, it is important to note that the big downward shifts in the stock of excess savings come from rising tax payments and personal consumption expenditures. The rise in consumption spending comes from a steady increase in services spending as the economy reopens, but the rise in taxes is a bit more complicated. Comprehensive data is unfortunately unavailable, but it looks as though rising tax receipts come from a combination of higher income taxes thanks to rising wages and employment levels alongside a jump in capital gains taxes given appreciating asset prices.

Now, it is important to mention here that it remains wrong to reason from accounting identities regarding excess savings. While it is true, definitionally, that for domestic savings to decrease then the savings of the government, foreign, or corporate sectors must increase it is not true that an increase in non-household savings will cause a decrease in domestic savings. As an example, an explicit attempt to increase the trade deficit could hypothetically result in lower domestic employment and lower government income tax revenues, thereby keeping aggregate domestic private savings constant even as the trade deficit increased.

It is also important to note the shifts in the composition of financial assets even as aggregate savings remain unchanged. The Federal Reserve’s Quantitative Easing can result in the transformation of household/corporate Treasury/MBS assets into bank deposits, and Quantitative Tightening can do the opposite. Indeed, that is exactly consistent with the real life bank deposit data; aggregate deposits increased tremendously at the beginning of the pandemic as QE went into effect and are decreasing now as QT goes into effect.

The Micro View

At some level, this should not be surprising. The top 1% hold about 30% of all assets in America, and the top 10% earn about 30% of all income. We know that these households have the highest discretionary spending budgets, lowest marginal propensity to consume, and were least likely to lose their jobs during the pandemic. It makes sense that they would end up holding the bulk of excess savings, stimulus checks or not. Critically, it’s also worth putting the $2.5 Trillion in excess savings in context against the $35 Trillion increase in aggregate household net worth—which also disproportionately benefitted higher-income and wealthier Americans.

Conclusions

Back in January, I laid out my thoughts on the future path of excess savings as such:

While most of the excess savings is held by high-income Americans, who do not have a particularly high marginal propensity to consume, I do think that high income earners will consume more of their excess savings than they would under a traditional increase in wealth. For one, high-income households have been stockpiling extremely liquid financial assets, an indication that they may only be waiting for a good opportunity to spend their money. For two, historical episodes of excess savings (like the post-WWII era) have seen a slow but steady drawdown of excess savings over the following 5-10 years. For three, polling from earlier this year showed that consumers intended to spend down some of their excess savings, and personal consumption expenditures have already exceeded their pre-COVID trend.
Generally, that trend has borne out over the last 6 months. Excess savings have decreased, driven by high-income households spending more (and being taxed on their income/capital gains). However, I underappreciated the downside risks to lower and middle-income household’s balance sheets—risks that have only intensified since then.
The good news on that front is, despite an appreciable slowdown in the labor market, employment levels and wage growth remain strong. Most Americans’ most valuable asset is their job, and that is especially true of lower and middle-income Americans. Without a job, however, they would not be able to rely on excess savings.

When it comes to China, official U.S. trade statistics miss a big part of the story. They don’t include the value of roughly 800 million shipments a year that arrive in the U.S. under a set of rules known as de minimis, a Latin phrase meaning items too small to be bothered with, that applies to individual imports entering the U.S. valued under $800. Correcting for that suggests the deficit with China might have barely budged, and possibly grown, since the trade war. The Journal obtained data from a Customs database of known de minimis shipments and their declared value. The data showed nearly 800 million such packages enter the U.S. annually—more than two million a day—with as much as half coming directly from China. The data show that the known reported value of shipments surged from $40.5 million in 2012 to a peak of $67 billion in fiscal year 2020, with $46 billion directly from China. (In 2021, as the pandemic faded, the known value of shipments declined to about $40 billion.)There are flaws in this data. Many packages enter via the postal service. In some cases, Customs only knows their weight, not the value. Data show the country from which a shipment entered the U.S., rather than the country of origin. This is part of why it hadn’t been publicly released;Customs is working to produce official statistics.

Josh Zumbrun, "The Tiny Loophole That Understates the Trade Deficit With China,"Wall Street Journal, June 17, 2022, https://www.wsj.com/articles/the-tiny-loophole-that-understates-the-trade-deficit-with-china-11655458201

The Tiny Loophole That Understates the Trade Deficit With China

American politicians and economists have obsessed over the U.S. trade deficit with China for years.

Lowering the deficit was one of former President Donald Trump’s goals in imposing tariffs on the majority of Chinese imports. President Biden’s administration is debating whether to peel them off to alleviate inflation or keep them on to pressure China. After all, they did appear to achieve one of Mr. Trump’s purposes: reducing the bilateral deficit between the U.S. and China, which according to the official U.S. trade statistics topped $400 billion a year in 2018.

But here’s the catch: When it comes to China, official U.S. trade statistics miss a big part of the story. They don’t include the value of roughly 800 million shipments a year that arrive in the U.S. under a set of rules known as de minimis, a Latin phrase meaning items too small to be bothered with, that applies to individual imports entering the U.S. valued under $800. Correcting for that suggests the deficit with China might have barely budged, and possibly grown, since the trade war.

“The whole idea of ‘de minimis’ is you send in packages, and Customs isn’t checking the values,” said Jeff Ferry, an economist at the Coalition for a Prosperous America, a trade group that supports tariffs as a tool to help U.S. firms compete with China.

“This has implications for whether we can trust the figures on imports from China” where such shipments have become especially common, said Mr. Ferry, who in a study earlier this year determined U.S. trade statistics omit the value of such shipments.

The rise of e-commerce made it easy to search online for low-cost goods shipped directly to your door. The U.S.-China trade war created a tremendous additional incentive. One reason you find such cheap goods online is because it is increasingly common to ship them into the country de minimis, avoiding tariffs. A U.S. retailer, by contrast, would have to pay the tariff on a large shipment of the same item.

For most of the decades since its creation by Congress in 1938, the use of de minimis was small—as trivial as intended. Congress stipulated the purpose was “to avoid expense and inconvenience to the government disproportionate to the amount of revenue that would otherwise be collected.”

The Journal obtained data from a Customs database of known de minimis shipments and their declared value. The data showed nearly 800 million such packages enter the U.S. annually—more than two million a day—with as much as half coming directly from China.

The data show that the known reported value of shipments surged from $40.5 million in 2012 to a peak of $67 billion in fiscal year 2020, with $46 billion directly from China. (In 2021, as the pandemic faded, the known value of shipments declined to about $40 billion.)

There are flaws in this data. Many packages enter via the postal service. In some cases, Customs only knows their weight, not the value. Data show the country from which a shipment entered the U.S., rather than the country of origin. This is part of why it hadn’t been publicly released; Customs is working to produce official statistics.

As de minimis went from trivial to substantial, the Census Bureau’s official trade statistics didn’t change to reflect this. (Other countries have their own de minimis rules, but U.S. exports that qualify for that treatment aren’t thought to be significant. China’s corresponding threshold is only 50 yuan, or about $7.45.)

The Census Bureau said in a written statement that shipments meeting the de minimis criteria aren’t received by Census, and thus not included in its estimation process. The Census declined to comment on estimates about the scale of de minimis, but said that if Customs published official de minimis statistics “we would intend to research the quality of those statistics to evaluate whether they could be incorporated into the official merchandise trade statistics.”

Mr. Ferry, the economist, estimates the true value could be nearly twice as high as what Customs data captures. He looked at Securities and Exchange Commission filings of the largest U.S. internet retailers, and calculated they are bringing in $112 billion of goods a year that likely qualify for de minimis. Even this figure doesn’t capture China-based companies whose entire business model relies on duty-free shipments, but don’t file with the SEC.

For example, officially, the U.S.-China goods deficit reached $402 billion in fiscal year 2018 but narrowed to $302 billion in 2020, as tariffs and then the pandemic damped trade.

But in fact, de minimis shipments surged during this period, precisely to dodge tariffs and because of online shopping during the pandemic. The different measures and estimates of de minimis are crude; they have none of the precision of our data on imports of, say, cars. But they imply the fiscal 2020 deficit was probably at least $350 billion and perhaps larger than ever at $430 billion.

During those early days of the pandemic, people were ordering everything online. It was taking a long time to arrive. How much came from China? Certainly a lot. More than the statistics capture. How much exactly? No one knows.

When President Trump entered the Oval Office, CBO projected the cumulative 2017-2027 budget deficits would be $10.0 trillion. When he left office four years later, CBO’s projected deficits for the same period were $13.9 trillion. The president signed or enacted $7.8 trillion in new initiatives, the costs of which were partially offset by $3.9 trillion saved from economic growth revenues and technical re-estimates of taxes and spending levels. Economic and technical factors produced a substantial $3.9 trillion in actual and projected savings over this period. Of this amount, $2.7 trillion comes from falling interest rate projections, which reduced the projected cost of net interest on the national debt. Another $1.3 trillion comes from higher tax revenues produced by faster economic growth projections.Technical re-estimates have reduced mandatory spending projections but also tax revenues. Most of these savings are projected to occur later in the 2017-2027 period and thus may not materialize if economic growth slows or interest rates rise. President Trump signed legislation and approved executive actions costing $7.8 trillion over the decade—compared to $5.0 trillion for President Obama and $6.9 trillion for President Bush, and he enacted these costs in just a single four-year presidential term, compared to his predecessors’ eight years in the Oval Office. The largest drivers were pandemic relief legislation ($3.9 trillion), the 2017 tax cuts ($2.0 trillion), and legislation raising the discretionary spending caps ($1.6 trillion). presidents are also limited to signing what Congress will pass. And during the Trump presidency, much of the $8 trillion in enacted legislation was passed by overwhelmingly bipartisan Congressional majorities.Consider the eight bills that Congress passed and President Trump signed costing at least $100 billion over the baseline, plus a separate pandemic relief bill (P.L. 166-159) that accelerated nearly $100 billion in previously enacted expenses. The most partisan of these nine bills was obviously the 2017 tax cuts (which also repealed the Obamacare individual mandate penalty). That law was passed exclusively by Republicans without a single Democratic vote. (See Figure 7 in the appendix for the largest bills signed during the Trump presidency.) The other eight bills—which consisted of five pandemic-relief laws, the 2018 and 2019 Bipartisan Budget Acts that raised the discretionary spending caps, and the 2019 law repealing key Obamacare taxes—were generally bipartisan. In fact, each of the eight bills passed the Senate with the majority support of both Republicans and Democrats. Senate Democrats cumulatively voted 94% in favor of these bills, and Senate Republicans cumulatively voted 76% in favor—not counting the CARES Act that passed the Senate by voice vote. A similar phenomenon occurred during the Obama presidency, when $5.0 trillion in enacted legislation was dominated by $4.1 trillion in bipartisan extensions of various current tax policies such as the 2001 and 2003 tax cuts. In fact, the $1 trillion stimulus legislation in 2009 was the only major deficit-hiking bill passed by an almost-exclusively Democratic majority during those eight years. Over the previous two presidencies, the vast majority of new deficit-expanding bills has been enthusiastically bipartisan.

Brian Riedl, "Trump’s Fiscal Legacy: A Comprehensive Overview of Spending, Taxes, and Deficits,"Manhattan Institute, May 12, 2022, https://www.manhattan-institute.org/trumps-fiscal-legacy

The surviving descendants of the old elite have prospered. By 2010 they were again richer and more educated than the Chinese average, according to data gathered by an international group of academics. Adding new evidence from cities to previous work, which looked at rural areas alone, the authors now find that the elite’s grandchildren have even out-earned Communist Party members.To measure the initial impact of China’s reforms on inequality, the authors compiled data on land ownership in 1950 from archived records. Unsurprisingly, inequality of land holdings, measured by a Gini coefficient, fell sharply after land reform, from 0.5 to 0.1—close to perfect equality. To measure what has happened since then, the authors used a survey of 36,000 Chinese residents from 2010. It recorded earnings and education, as well as social class. The party created hereditary class labels in 1950, in part to punish the old guard. These allowed the researchers to distinguish between old elites and everyone else. The authors found that elites born before 1940 were 7% likelier than their contemporaries to have finished secondary school. Their stigmatised children were 3% less likely to have done so than others their age. By 2010 the children of old elites earned 5% less than other Chinese. But things flipped back. Descendants of the old elite born between 1966 and 1990 were 6% more likely to finish high school than their contemporaries. In 2010 they earned 12% more than other Chinese. They even earned 2% more than party members. The researchers found that the old elite’s grandchildren are more enterprising and work longer hours than the descendants of those who had lower social standing. Although the elite’s capital was destroyed 70 years ago, their social capital has endured.

The authors found that elites born before 1940 were 7% likelier than their contemporaries to have finished secondary school. Their stigmatised children were 3% less likely to have done so than others their age. By 2010 the children of old elites earned 5% less than other Chinese.

But things flipped back. Descendants of the old elite born between 1966 and 1990 were 6% more likely to finish high school than their contemporaries. In 2010 they earned 12% more than other Chinese. They even earned 2% more than party members.

The researchers found that the old elite’s grandchildren are more enterprising and work longer hours than the descendants of those who had lower social standing. Although the elite’s capital was destroyed 70 years ago, their social capital has endured.

Economist Staff, “The grandchildren of China’s pre-revolutionary elite are unusually rich,”The Economist, June 9, 2022, https://www.economist.com/graphic-detail/2022/06/09/the-grandchildren-of-chinas-pre-revolutionary-elite-are-unusually-rich

The grandchildren of China’s pre-revolutionary elite are unusually rich

The land ownership system of feudal exploitation by the landlord class shall be abolished.” So read China’s agrarian reform law of 1950. Land was seized from the better-off and given to poor farmers, whose share of farmland rose from 14% in 1947 to 47% in 1954. Liu Shaoqi, who was president during the Mao era, called it the “most thorough reform in thousands of years of Chinese history”.

Hundreds of thousands of landlords were murdered. Tens of millions of people died in a famine when farms were collectivised. Yet the surviving descendants of the old elite have prospered. By 2010 they were again richer and more educated than the Chinese average, according to data gathered by an international group of academics. Adding new evidence from cities to previous work, which looked at rural areas alone, the authors now find that the elite’s grandchildren have even out-earned Communist Party members.

To measure the initial impact of China’s reforms on inequality, the authors compiled data on land ownership in 1950 from archived records. Unsurprisingly, inequality of land holdings, measured by a Gini coefficient, fell sharply after land reform, from 0.5 to 0.1—close to perfect equality.

To measure what has happened since then, the authors used a survey of 36,000 Chinese residents from 2010. It recorded earnings and education, as well as social class. The party created hereditary class labels in 1950, in part to punish the old guard. These allowed the researchers to distinguish between old elites and everyone else.

We are all familiar with comparisons showing that Black people earn 50% less than white peers and women earn 70 cents for every dollar that a man earns. However, the most popular tools used to combat disparities in the workplace have produced almost no measurable results. The average impact of corporate diversity, equity, and inclusion (DEI) training is zero and some evidence suggests that the impact can become negative if the training is mandated.One of the most important developments in the study of racial inequality has been the quantification of the importance of pre-market skills in explaining differences in labor market outcomes between Black and white workers. In 2010, using nationally representative data on thousands of individuals in their 40s, I estimated that Black men earn 39.4% less than white men and Black women earn 13.1% less than white women. Yet, accounting for one variable-educational achievement in their teenage years--reduced that difference to 10.9% (a 72% reduction) for men and revealed that Black women earn 12.7 percent more than white women, on average. Derek Neal, an economist at the University of Chicago, and William Johnson were among the first to make this point in 1996: “While our results do provide some evidence for current labor market discrimination, skills gaps play such a large role that we believe future research should focus on the obstacles Black children face in acquiring productive skill.”

Roland Fryer, "It’s time for data-first diversity, equity, and inclusion,"Fortune, June 20, 2022, https://fortune.com/2022/06/20/data-first-diversity-equity-inclusion-careers-black-workers-gender-race-bias-dei-roland-fryer/

It’s time for data-first diversity, equity, and inclusion

I have spent hundreds of hours in diversity training over the past two decades-from descriptions of federal anti-discrimination laws to academic-style seminars on the perils of implicit bias, microaggressions, or misgendering.

Advocates of this kind of training have their hearts in the right place. We are all familiar with comparisons showing that Black people earn 50% less than white peers and women earn 70 cents for every dollar that a man earns.

However, the most popular tools used to combat disparities in the workplace have produced almost no measurable results.

The average impact of corporate diversity, equity, and inclusion (DEI) training is zero and some evidence suggests that the impact can become negative if the training is mandated.

“Statistical Snapshots,” which describe how employee outcomes differ by demographic group, are another popular tool. These numbers cannot provide proof of bias. Simple averages often mislead and, importantly, crafting strategies based on misleading data often does more harm than good.

Some business leaders, in their determination to increase diversity, leap directly from observing raw disparities to removing some information from application forms, another common practice meant to make workplaces more equitable. However, hiding information on applications often leads to worse outcomes for those it was intended to help-likely because hiring managers use race itself as a proxy for the information they’re no longer allowed to see.

Our intuition for how to decrease race and gender disparities in the workplace has failed us for decades. It’s time to stop guessing and start using the scientific method. Remember when we thought that the Bubonic Plague was caused by a triple conjunction of Saturn, Jupiter, and Mars in the 40th degree of Aquarius?

Here is a three-step approach that can turn earnest intentions into good science.

Understand disparities

For decades, social scientists have shown that raw gaps in employment outcomes like hiring or wages-the type of data typically provided to C-suite executives-misstate the amount of actual bias in an organization. This data omits many factors that are key to personnel decisions, factors that often vary by group, owing to disparities in society at large. Business leaders can and should work to address inequality in their communities, but they should not mistake society-wide gaps for bias by their own employees.

One of the most important developments in the study of racial inequality has been the quantification of the importance of pre-market skills in explaining differences in labor market outcomes between Black and white workers. In 2010, using nationally representative data on thousands of individuals in their 40s, I estimated that Black men earn 39.4% less than white men and Black women earn 13.1% less than white women. Yet, accounting for one variable-educational achievement in their teenage years--reduced that difference to 10.9% (a 72% reduction) for men and revealed that Black women earn 12.7 percent more than white women, on average. Derek Neal, an economist at the University of Chicago, and William Johnson were among the first to make this point in 1996: “While our results do provide some evidence for current labor market discrimination, skills gaps play such a large role that we believe future research should focus on the obstacles Black children face in acquiring productive skill.”

Recently, I worked with a network of hospitals determined to rid their organization of gender bias. The basic facts were startling: Women earned 33% less than men when they were hired and their wages increased less than men once on the job. Yet, accounting for basic demographic variables known about individuals prior to hiring, these differences decreased by 74%. A problem remained, but it was an order of magnitude smaller than the unadjusted numbers implied.

Find the root causes of bias

Social scientists tend to categorize bias into one of three flavors: preference, information, and structural. Preference bias is good old-fashioned bigotry. If company A prefers group W over group B then they will hire and promote them more even if they are similarly qualified.

Information bias arises when employers have imperfect information about workers’ potential productivity and use observable proxies, like gender or race, to make inferences (gender stereotypes are a classic example).

Structural bias occurs when companies institute practices, formally or informally, that have a disparate impact on particular groups, even when the underlying practices are themselves group blind. Employee referral programs can fall into this category.

Over the past fifty years, economists and other social scientists have developed brilliant ways of statistically distinguishing between different types of bias. Gary Becker, in his 1993 Nobel Prize acceptance speech, outlined one such statistical procedure known as the “outcomes test.” It operates by comparing the success rate of decisions across groups and then inferring whether different decision rules were used for different groups. For example, if women CEOs statistically outperform male CEOs, all else equal, that would suggest that a higher standard was applied to women in the selection process. This type of statistical test can be used for hiring, promotions, and attrition across an organization.

In 2013, collaborators and I developed a similar test to detect information-based bias. Our approach uses the insight that if employers have information-based biases at the time of hiring, but then learn more about an employee’s productivity once they are on the job, one would expect to see the returns to tenure within the company to be higher for the group that faced the initial bias. Using a nationally representative dataset of thousands of individuals, we found that there was a significant gap at the time of hiring for Black candidates relative to white peers but that, as predicted, Black candidates experienced a 1.1 percentage point higher return to tenure.

With the aforementioned hospital network, the data pointed to a structural bias in scheduling. Women and men who worked the same number of hours earned exactly the same wage, but men worked more hours due to how the company assigned schedules, not women’s desire to work less.

This is the key step that is missing in every DEI initiative I have seen in the past 25 years: a rigorous, data-driven assessment of root causes that drives the search for effective solutions. In other aspects of life, we would not fathom prescribing a treatment without knowing the underlying cause. Hiding information on resumes when information bias is present is as effective as using alcohol baths to treat fever.

Evaluate

We must rigorously evaluate what works and what doesn't. The old cardiac test-where you “feel it in your heart”-is not good enough. Once we know where potential biases exist, determine what caused them, and curate a set of solutions to test, we must meticulously evaluate what’s working and what’s not.

Solutions that yield measurable results can be substantiated into company policy, while those that don't should be discarded. In the case of the hospital network, once a small change was made to the structure of their scheduling, gender differences were reduced. Despite countless hours spent in training and seminars, their results were unchanged for years. The solution was hidden in plain data.

This will seem heretical to some-but it barely scratches the surface of what's possible with a data-first approach to diversity, equity, and inclusion. More corporate leaders should be trying to solve diversity challenges in the same way they solve problems in every other aspect of their business: through intelligent use of data, rigorous hypothesis testing, and honest inference about what is working.

We examine theinfluence of physical proximity on between-startup knowledge spillovers at one of the largest technology co-working hubs in the United States. Relying on the random assignment of office space to the hub's 251 startups, we find that proximity positively influences knowledge spillovers as proxied by the likelihood of adopting an upstream web technology already used by a peer startup. This effect is largest for startups within close proximity of each other and quickly decays: startups more than 20 meters apart on the same floor are indistinguishable from startups on different floors. The main driver of the effect appears to be social interactions. While startups in close proximity are most likely to participate in social co-working space events together,knowledge spillovers are greatest between startups that socialize but are dissimilar.Ultimately, startups that are embedded in environments that have neither too much nor too little diversity perform better, but only if they socialize.

Maria P. Roche, Alexander Oettl and Christian Catalini, "(Co-)Working in Close Proximity: Knowledge Spillovers and Social Interactions,"National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30120

Core Findings

“…The setting for our study is one of the largest technology co-working spaces in the United States. The building consists of five floors, covering 9,300 m2 (100,000 sq.ft.). One challenge in examining the relationship between location and knowledge spillovers is that rms and individuals may choose to locate in areas where knowledge exchange is already likely to be high. To deal with this potential endogenous location choice, we rely on the random assignment of office space to the hub's 251 startups. We measure knowledge spillovers as the instance of adopting a component of a peer rm's technology stack. Using floor plans to measure geographic distance, we find that close physical proximity greatly influences the likelihood of these knowledge spillovers. This effect, however, quickly decays with distance where startup rms that are more than 20 meters (66 feet) away are no longer influenced by each other. Strikingly, being located more than 20 meters apart, but on the same floor does not appear to differ from being located on a different floor altogether. In addition, we find that when firms overlap with common areas at the hub (e.g., kitchens), the distance of influence increases, revealing the important role that these spatial features play in extending geographic reach and in promoting knowledge spillovers..”

The Evidence

“. .Table 3 presents the results from assessing the effect of distance on the amount of peer technology adoption (ln(AdoptCountij + 1)) using a standard OLS model and using a linear probability model to estimate the likelihood of adopting a technology from a peer rm 1(AdoptTechij ). In the full model (Columns 2 and 4), using rm-x-room fixed effects and controlling for industry, business model, gender, age and pre-period technology overlap, we find that the doubling of distance between two dyads reduces both the amount of peer technology adoption by 3.5% and the likelihood of any peer technology adoption by 1.7%, with both point estimates significant at the 1% level. As seen, the magnitude and statistical significance of the effect remains largely unchanged with the inclusion of additional controls….”

New York — which is where I happen to live — isn’t just safe relative to other big cities. The data in the above chart are from the Federal Bureau of Investigation and local police departments, but the Centers for Disease Control and Prevention also keep track of homicides as reported on death certificates, and one can sort them by, among other things, whether they befell residents of a county that contains all or part of the biggest city in a large (population of one million or more) metropolitan area, a fringe (suburban) county in such a metropolitan area, or elsewhere. Suburban counties in large metro areas have tended to have the lowest homicide rates, but New York City’s was even lower from 2017 to 2019 and was only slightly higher in 2020, the most recent year for which the CDC has full data. Some causes of death other than homicide seem pretty relevant to assessing safety. Your risk of getting killed on the subway or in a subway station is much higher in New York City than in a place with no subway, for example,but if you add up all risks related to getting to work or school or wherever else it is you need to go, it’s a different story. New Yorkers are only about as third as likely to die in transportation accidents of any kind as Americans are overall. Put homicide and transportation risks together, and New York starts looking like a refuge from the American carnage.All these accountings leave out the September 2001 terrorist attacks. The most dangerous areas are a more hardscrabble lot, although led by an affluent coastal metro (Baltimore-Columbia) where the main city has an awful violent crime problem. The city of Baltimore’s homicide rate was a staggering 58.5 per 100,000 residents in 2021, twice that of Chicago and more than 10 times New York City’s.

Justin Fox, "New York City Is a Lot Safer Than Small-Town America,"Bloomberg, June 7, 2022, https://www.bloomberg.com/opinion/articles/2022-06-07/is-new-york-city-more-dangerous-than-rural-america

New York City Is a Lot Safer Than Small-Town America

Since the summer of 2020 the United States has been experiencing a deadly wave of violence, and New York City has not been immune. The number of homicides in the city shot up 47% in 2020, and rose another 4% last year. While the tally is down 9% so far this year there have been some shocking murders, not to mention a mass shooting on the subway that miraculously didn’t take any lives.

Regular consumers of the news would thus be forgiven for believing that the Big Apple has become a terribly dangerous place. And yes, it is markedly more dangerous than it was before the pandemic. I’m not here to dismiss concerns about rising crime in the city. But a little context is in order. And by “a little context,” I mean a lot of charts.

The historical context, which a fair number of people are aware of, is that New York’s pandemic murder wave followed a long decline, with the city’s homicide rate in 2021 still less than a fifth what it was in 1990. The geographical context, which seems to be less widely understood, is that while homicide rates fell sharply all over the the US in the 1990s, that decline slowed in the 2000s and reversed starting in 2015. In Philadelphia the homicide rate is now worse than it was in the early 1990s, and in Chicago it’s close. Which means there’s a growing gap between New York City and most of the rest of urban America.

Some causes of death other than homicide seem pretty relevant to assessing safety. Your risk of getting killed on the subway or in a subway station is much higher in New York City than in a place with no subway, for example, but if you add up all risks related to getting to work or school or wherever else it is you need to go, it’s a different story. New Yorkers are only about as third as likely to die in transportation accidents of any kind as Americans are overall. Put homicide and transportation risks together, and New York starts looking like a refuge from the American carnage.

There are other accidental threats listed in ICD-10 that seem less salient in the city than in the country, and in some cases, the suburbs. To name a few: “contact with powered lawnmower,” “drowning and submersion while in swimming pool,” “contact with venomous snakes and lizards,” “victim of volcanic eruption.” So I ran the numbers again for mortality from all external causes.

The explosion of drug-overdose deaths in the US over the past decade is one of the most depressing trends one encounters in CDC Wonder, but it does take us somewhat afield from what people usually have in mind when they consider whether a place is safe or dangerous. Same goes for suicides, which have also risen but not as sharply. Then there are external causes such as falls and surgical complications that are usually fatal only for the very old. If included in the calculations, they tend to make retirement communities look terribly dangerous, which doesn’t seem right.

Here’s what mortality from external causes looks like if one excludes (1) accidental poisoning (how drug overdoses are classified), (2) intentional self-harm, (3) falls, (4) complications of medical and surgical care and (5) sequelae of external causes of morbidity and mortality (after-effects, that is, which are also fatal mostly for the elderly).

This is so opposite from conventional wisdom that it’s worth teasing out why. One obvious explanation is that murder scares people a lot more than accidents do, and homicide risk is in fact highest in urban counties — just not in New York City. Another is that, as a colleague put it to me, you may be less likely to be victim of a crime in New York City, but you’re more likely to witness one. In dense urban environments you simply see a lot more of what’s going on, for good and ill. It’s also possible that the issue is not so much urban versus rural as it is who lives where. An affluent rural or exurban county where lots of people can work from home might be lower risk than a poorer urban one where most can’t. Rural counties in Appalachia or the Deep South might have very different profiles from those in New England or the Plains states.

A look at 2020 selected-external-causes mortality by state offers some support for the latter interpretation. The Northeast is the safest part of the country, but some heavily rural states (Mississippi, Arkansas) are a lot more dangerous than others (Vermont, Iowa).

But let’s not forget those data limitations. While CDC Wonder knows everything about mortality in the US, for privacy reasons it only shares what it knows if there are 10 or more deaths to report, and because of reliability concerns it only calculates mortality rates if there are 20 or more deaths. For the collection of external causes I selected, Wonder churned out 2020 death totals for 1,711 of the nation’s 3,143 counties and county equivalents (parishes in Louisiana, census areas in Alaska, independent cities in Virginia and some other states) and mortality rates for 997. It provided enough data to calculate the mortality rates for all 1,711, but I chose not to for the charts here. If I had, three more Northern Virginia cities and counties would have entered the top 10 along with Story County, Iowa, and La Crosse County, Wisconsin.

If I selected a wider array of external causes, or combined data from several years, more counties with smaller populations would enter the mix. Still, pretty much any version of the most-dangerous-counties list would be at least as heavy on rural counties as this.

Also, because complete 2020 Census results have yet to be released, the CDC is still using outdated 2020 population estimates based on the 2010 census. For New York City, this estimate is 550,977 lower than the number from the 2020 census, and the CDC estimates for most of the 2010s are probably too low as well. To give a sense of the impact, the city’s CDC-calculated homicide rate of 5.4 per 100,000 in 2020 would drop to 5.1 using the census population. I decided to stick with the CDC’s numbers nonetheless because (1) the city likely lost several hundred thousand residents in the months following the April 1 census record date, and (2) redoing all the 2020 mortality-rate calculations using census data would have introduced a lot of opportunities for error. But keep in mind that all the mortality rates for New York City in recent years in the CDC-derived charts are probably slight overestimates.

Finally, it would be nice to see this data by metropolitan area, wouldn’t it? CDC Wonder’s most up-to-date deaths databases does not provide statistics by metro area. It is possible to download them by counties, then sort those into metropolitan areas, but with 2020 deaths data from my list of external causes available for only a little over half of US counties, that doesn’t really work. By combining deaths from the five most recent available years I was able to get a more complete county list, and fill in the blanks with a little extra effort. Here, then, are the safest metropolitan areas in the US from 2016 through 2020, by my admittedly somewhat idiosyncratic definition.

The most dangerous areas are a more hardscrabble lot, although led by an affluent coastal metro (Baltimore-Columbia) where the main city has an awful violent crime problem. The city of Baltimore’s homicide rate was a staggering 58.5 per 100,000 residents in 2021, twice that of Chicago and more than 10 times New York City’s.

Data sources for charts

Safest of the Six Largest U.S. Cities: The main data sources here are the FBI’s Crime Data Explorer and Census Bureau annual population estimates that I got from the Data Commons Place Explorer (for pre-2010 numbers) and the Census Bureau (for 2010-2021). I could not find city population estimates for 1985-1989 so I calculated my own by just assuming the cities grew (or shrank) at a uniform pace between the 1980 and 1990 censuses. The FBI hasn’t released 2021 crime data yet so I got that year’s numbers either directly from police departments or from news reports based on police department data. Also, for some reason the FBI’s post-2014 homicide numbers for Philadelphia are different from (and lower than) those on the Philadelphia Police Department website, so I went with the latter.

Murder in the Cities, Suburbs and Country: This is downloaded straight from CDC Wonder’s Underlying Causes of Death database, but there are two different ways to get homicide totals from that. One is to look under “Injury Intent and Mechanism” and select “homicide” as the injury intent, the other is to look through the ICD-10 codes used to classify all causes of death and select “assault” under “External causes of morbidity and mortality.” The former includes deaths from terrorist attacks and the latter does not, because they are classified under a separate ICD-10 category of “Codes for special purposes.” When this piece was first published, the chart showed homicide deaths with the September 2001 terrorist attacks subtracted out (for consistency with the FBI data and because otherwise New York’s high 2001 rate would make the chart very hard to read); subsequently I realized it would be simpler and more consistent with the other charts to use the assault numbers, which are almost but not quite identical. Here they are by county urbanization status (I combined the two categories of nonmetro counties for the charts) and for New York City. Once you click through, you’ll have to agree to a couple of data use restrictions, then wait as CDC Wonder generates the data.

Car Crashes Change the Picture: This adds together assault plus transportation accident deaths as reported under “External causes of morbidity and mortality.” Here they are by county urbanization, and for New York City.

Safer in the Big City: This is all “External causes of morbidity and mortality,” by county urbanization, and for New York City.

Still Safer in the Big City: This is external causes minus the categories discussed in the text and the chart footnotes, by county urbanization and for New York City.

Safe States and Dangerous Ones: Selected external causes by state for 2020.

The Safest Counties in America, Sort Of, and The Most Dangerous Counties in America, Sort Of: Selected external causes by county for 2020.

In the aftermath of the Covid-19 pandemic, the U.S. equity markets have witnessed a surge in the number of publicly listed companies. Using data for the three major U.S. stock exchanges (AMEX, NYSE, and NASDAQ), we find that the number of publicly traded companies went from 4,144 at the end of August 2020 to 5,301 at the end of December 2021, a staggering increase of about 28 percent.Figure 1 reports the trend in the number of public companies since 1973, two years after the NASDAQ started its operations. The number of publicly traded firms peaked in 1997, reaching 7,842 at the end of November, and has since steadily declined through 2012, after which it remained nearly flat until about the first half of 2020. Given the importance of the SPAC phenomenon, we manually classify the newly listed companies into SPACs and non-SPACs, that is, companies that go public with a traditional IPO process, and report in Figure 3 the number of newly listed firms in each category. During the period from the end of August 2020 to December 2021, there were 736 newly listed firms classified as SPACs and 646 newly listed non-SPAC firms; that is, SPACs accounted for about 53% of total newly listed firms.The number of newly listed SPACs took off in the second half of 2020 and peaked in the months of March and April 2021 We document that the entry of SPACs has significantly modified the composition of U.S. equity markets over the period from August 2020 to the end of 2021. First, the fraction of small size stocks classified as SPACs had increased from about 8% to about 28%. Second, the fraction of ordinary common shares of foreign-incorporated companies has jumped from about 14 percent to about 18 percent. Third, at the end of 2021, companies in the Other Financial Investment Activities industry (4-digit NAICS code 5239) represented 15 percent of listed companies, up from about 4 percent in August 2020.

Christine Dobridge, Rebecca John and Berardino Palazzo, "The post-COVID stock listing boom,"Federal Reserve Board, June 17, 2022, https://www.federalreserve.gov/econres/notes/feds-notes/the-post-covid-stock-listing-boom-20220617.htm

Introduction

In the aftermath of the Covid-19 pandemic, the U.S. equity markets have witnessed a surge in the number of publicly listed companies. Using data for the three major U.S. stock exchanges (AMEX, NYSE, and NASDAQ), we find that the number of publicly traded companies went from 4,144 at the end of August 2020 to 5,301 at the end of December 2021, a staggering increase of about 28 percent.1

Figure 1 reports the trend in the number of public companies since 1973, two years after the NASDAQ started its operations. The number of publicly traded firms peaked in 1997, reaching 7,842 at the end of November, and has since steadily declined through 2012, after which it remained nearly flat until about the first half of 2020.2

Figure 2 reports the 12-month moving average of the monthly growth rate for the NASDAQ and NYSE+AMEX, respectively. As the figure shows, the recent increase in the number of companies is a phenomenon that is equally important for firms traded in the NASDAQ and firms traded in the NYSE and AMEX platforms.

What is responsible for the recent listing boom?

The obvious answer to the above question is the huge popularity of investing in Special Purpose Acquisition Companies (SPACs). Starting in the second half of 2020, U.S. equity markets have witnessed an explosion in the number of so-called blank check companies' (or SPACs') stocks. These companies are effectively holding cash to acquire private companies and take them public, bypassing the traditional IPO process.3

Given the importance of the SPAC phenomenon, we manually classify the newly listed companies into SPACs and non-SPACs, that is, companies that go public with a traditional IPO process, and report in Figure 3 the number of newly listed firms in each category.4 During the period from the end of August 2020 to December 2021, there were 736 newly listed firms classified as SPACs and 646 newly listed non-SPAC firms; that is, SPACs accounted for about 53% of total newly listed firms. The number of newly listed SPACs took off in the second half of 2020 and peaked in the months of March and April 2021.5

Newly listed firms during the post-Covid listing boom: Some stylized facts

In what follows, we compare the characteristics of the about 1,400 newly publicly listed companies that entered the U.S. equity markets during the SPAC listing boom, between the end of August 2020 and December 2021, with the approximately same number of newly publicly listed companies that entered over the period December 2014 to August 2020.

Table 1 shows that companies that entered during the SPAC listing boom tended to have the same size distribution of the companies that had entered since the end of 2014; that is, about 30% of newly listed firms are small in terms of their market capitalization.7 However, Table 1 highlights that there is a significant difference in the size distribution of SPACs versus non-SPACs, with the former tending to be significantly smaller: out of 10 newly listed SPACs, 9 belong to the small size category.

Table 4 also highlights that SPAC stocks are more volatile after the announcement (but before De-SPAC). Figure 4 helps us understand why this is the case. We consider only the subset of SPACs that entered between the end of August 2020 and December 2021, have become an operating company, and depict some of the quantities reported in Table 4 around the period that goes from 60 days prior to 180 days after the day of the deal announcement. The shaded area is the interdecile range, the solid blue line is the median daily return, and the red dashed line is the fraction of SPACs that have become an operating company.

Concluding remarks

The number of publicly listed firms in the three major U.S. stock exchanges increased by about 1,100 in the period August 2020 to December 2021. This surge has been driven both by an unprecedented increase in the number of SPAC listings and by a large inflow of more traditional companies, thus suggesting that the post-COVID financial market environment has been particularly favorable for new IPOs amid low interest rates and ample market liquidity.

We document that the entry of SPACs has significantly modified the composition of U.S. equity markets over the period from August 2020 to the end of 2021. First, the fraction of small size stocks classified as SPACs had increased from about 8% to about 28%. Second, the fraction of ordinary common shares of foreign-incorporated companies has jumped from about 14 percent to about 18 percent. Third, at the end of 2021, companies in the Other Financial Investment Activities industry (4-digit NAICS code 5239) represented 15 percent of listed companies, up from about 4 percent in August 2020.

These compositional changes are very likely to be temporary since most SPACs are required to enter into a merger usually within 2 years from the IPO (or the SPAC must return shareholders' investments). As a consequence, we expect the fraction of firms in the Other Financial Investment Activities industry sector to decline as SPACs merge with companies operating in different industries. At the same time, we expect the fraction of ordinary common shares of foreign-incorporated companies to decline as more SPACs will become operating companies incorporated in the United States.

The fraction of SPACs in the small size stock category will also decrease. However, in the near future, having a large fraction of SPACs in small size portfolios might have implications for cross-sectional asset pricing given the peculiar properties of SPACs' returns before these investment vehicles become operating companies.9

References

Bulley, David and Dean Bennet (2020)," The use of Cayman Islands Structures for SPAC IPOS," webpage:https://www.applebyglobal.com/publications/the-use-of-cayman-islands-structures-for-spac-ipos/.

Doidge, Craig G., Andrew Karolyi, and René M. Stulz (2017), "The U.S. listing gap," Journal of Financial Economicshttps://www.sciencedirect.com/journal/journal-of-financial-economics, vol. 123 (3),pp. 464-487.https://doi.org/10.1016/j.jfineco.2016.12.002

Doidge, Craig G., Andrew Karolyi, and René M. Stulz (2004), "New lists: Fundamentals and survival rates," Journal of Financial Economicshttps://www.sciencedirect.com/journal/journal-of-financial-economics, vol. 73 (2),pp. 229-269.https://doi.org/10.1016/j.jfineco.2003.04.001

Gahng, Minmo, Jay R. Ritter, and Donghang Zhang (2021), "SPACs." Available at SSRN:https://ssrn.com/abstract=3775847.

________________________________

1. We measure the number of traded firms as the number of unique PERMCOs in the CRSP database with a non-missing value for price and number of shares outstanding. We only consider ordinary common shares of companies incorporated in the U.S. and outside the U.S. (share codes in CRSP equal to 10, 11, and 12) and traded in the three major U.S. stock exchanges: NYSE, AMEX, and NASDAQ (exchange codes in CRSP equal to 1, 2, and 3, respectively). If a SPAC changes its PERMCO after an acquisition, we do not consider the new PERMCO to be a new traded entity. Return to text

2. Fama and French (2004) suggest that the increase in the number of publicly listed firms in the period 1980-2001 is due to a decline in the cost of equity that has prompted smaller and more financially fragile firms to go public. Doidge, Karolyi, and Stulz (2017) calculate that about 46% of the decline in the number of publicly traded firms from 1996 through 2012 was due to delisting, while the remaining fraction was due to a lower listing rate. Return to text

3. Santilli, Peter and Amrith Ramkumar (2021), "SPACs Are the Stock Market's Hottest Trend. Here's How They Work," The Wall Street Journal, March 29, https://www.wsj.com/articles/spacs-are-the-stock-markets-hottest-trend-heres-how-they-work-11617010202. With the total number of publicly listed firms increasing by 1,157 from the end of August 2020 to the end of December 2021 and 1,382 new entrants over the period, we estimate that 225 firms delisted over the period. Return to text

4. We used the NASDAQ IPO calendar to manually classify newly listed firms into SPACs and non-SPACs (https://www.nasdaq.com/market-activity/ipos). We also use data from the website spacresearch.com (https://www.spacresearch.com/) to identify SPACs that have finalized an acquisition by the end of February 2022. In this note, we focus on the first time a SPAC appears on one of the three major U.S. stock exchanges. Return to text

5. Gahng, Ritter, and Zhang (2021) provide one of the first analysis of SPACs in U.S. equity markets. Their sample of SPACs ends in December 2019, prior to the recent SPAC listing boom. Return to text

6. Specifically, the number of non-SPAC firms increased from 3,952 in August 2020 to 4,377 in December 2021, an increase of about 11 percent. Return to text

7. We measure size using market capitalization at the end of the first month of trade. Small firms belong to the bottom quintile of the market capitalization distribution. Market capitalization quintiles are calculated each month using only companies traded on the New York Stock Exchange. Return to text

8. Many SPACs choose to incorporate in the Cayman Islands to take advantage of their more favorable statutory merger regime (Bulley and Bennet, 2020). Return to text

9. Excluding SPACs from portfolio formation before they become an operating company could be a potential solution to mitigate their effect on the return properties of small size portfolios. Return to text

The old wisdom that the more women pursue a career, the fewer children they bear, has long been shown to be false. In the 1980s, it was true that the higher a country’s female labour force participation rate, the lower its fertility rate. But a decade later, the relationship had flipped: the higher a country’s female labour participation rate, the higher its fertility rate. In some countries, the probability of having a second child is now highest among the professional classes and lowest among the lowest-skilled. In societies where male contributions are minimal, large numbers of women say they do not want another child despite their partner’s wishes. The result is that in countries where men do a third of childcare and housework, total fertility rates are roughly a third higher than in those where they do just a fifth.In the US, between May and December 2020, fathers spent 50 minutes per day in which their primary activity was childcare. During the first year of the pandemic this fell to 46 minutes, while mothers’ daily childcare commitments rose from 88 to 101 minutes. And the “new normal” beginning to take shape has seen US mothers’ labour force participation rates fall, reversing decades-long trends. The participation rate for fathers of young children dipped from 95 to 93.4 per cent in 2020, before recovering slightly to 93.9 last year. Mothers, meanwhile, have fallen from 66.4 to 65.8 per cent in 2020, labour participation rates slid further to 65.6 per cent last year, while in the UK female participation has also dipped slightly.

Change is needed on multiple fronts. Government and company policies must offer more flexibility for all parents. Britons need far more help meeting the country’s world-leading (in a bad way) childcare costs. And men must take their own step back from the office and join their partners in pulling their weight at home. Everyone must shoulder the burden of raising the next generation.

And the “new normal” beginning to take shape has seen US mothers’ labour force participation rates fall, reversing decades-long trends. The participation rate for fathers of young children dipped from 95 to 93.4 per cent in 2020, before recovering slightly to 93.9 last year. Mothers, meanwhile, have fallen from 66.4 to 65.8 per cent in 2020, labour participation rates slid further to 65.6 per cent last year, while in the UK female participation has also dipped slightly.

What’s worrying is that in parts of the west, men have not just been slow to act on this, but the trend has been in the opposite direction. In the US, between May and December 2020, fathers spent 50 minutes per day in which their primary activity was childcare. During the first year of the pandemic this fell to 46 minutes, while mothers’ daily childcare commitments rose from 88 to 101 minutes.

For decades, the assumption has been that when it comes to the workplace, out of sight is out of pocket, but the pandemic has shown otherwise. The new era of flexible working makes the equalisation of parental childcare more feasible than ever.

John Burn-Murdoch, "Men stepping up at home is key to boosting birth rates,"Financial Times, June 16, 2022, https://www.ft.com/content/6a6a7695-4756-42d1-b03d-283ad5e57d5f

Men stepping up at home is key to boosting birth rates

When we talk about how to address falling birth rates, it’s not surprising that the conversation usually centres on young women. After all, it’s quite tricky to influence birth rates without them. But according to a growing body of research, this demographic is already doing about as much as they can. A more fruitful policy target may in fact be men.

In the year 2000, there were 22.5 people aged 65 and older for every 100 people of working age in OECD countries. This year, the ratio is estimated to have climbed by almost half to 32.4. Over the same period, the female labour force participation rate climbed steadily from 59 to 65 per cent, and the gender pay gap for workers in OECD countries shrunk from 18 per cent to just under 12 per cent.

The coinciding trends present a conundrum: at a time when some governments are fretting about declining birth rates, the number of women navigating the fraught trade-off between flourishing professionally and having children has never been larger.

A comprehensive new study suggests it is time we stop defaulting to the assumption that job vs family is a trade-off solely for women, and start paying more attention to the role men can play in facilitating simultaneous advances in both birth rates and female employment.

The old wisdom that the more women pursue a career, the fewer children they bear, has long been shown to be false. In the 1980s, it was true that the higher a country’s female labour force participation rate, the lower its fertility rate. But a decade later, the relationship had flipped: the higher a country’s female labour participation rate, the higher its fertility rate. In some countries, the probability of having a second child is now highest among the professional classes and lowest among the lowest-skilled.

Several factors have contributed to this reversal, including improvements in state provision of and support for childcare, but among the biggest shifts has been the increase in paternal contribution to childcare and housework. This makes the trade-off for mothers much less steep.

Having a child is usually dependent on both partners wanting one. Research shows that agreement tends to be much higher where men do relatively high shares of childcare. In societies where male contributions are minimal, large numbers of women say they do not want another child despite their partner’s wishes. The result is that in countries where men do a third of childcare and housework, total fertility rates are roughly a third higher than in those where they do just a fifth.

Does screening applicants using exams help or hurt the chances of lower-SES candidates? Because individuals from lower socioeconomic backgrounds fare, on average, worse than those from richer backgrounds in standardized tests, a common concern with this "meritocratic" approach is that it might have a negative impact on the opportunities of lower-SES individuals. However, an alternative view is that, even if such applicants underperformed on exams, other (potentially more discretionary and less impersonal) selection criteria might put them at an even worse disadvantage. We investigate this question using evidence from the 1883 Pendleton Act, a landmark reform in American history which introduced competitive exams to select certain federal employees. Using newly assembled data on the socioeconomic backgrounds of government employees and a difference-in-differences strategy, we find that, although the reform increased the representation of "educated outsiders" (individuals with high education but limited connections), it reduced the share of lower-SES individuals. This decline was driven by a higher representation of the middle class, with little change in the representation of upper-class applicants. The drop in the representation of lower-SES workers was stronger among applicants from states with more unequal access to schooling as well as in offices that relied more heavily on connections prior to the reform. These findings suggest that, although using exams could help select more qualified candidates, these improvements can come with the cost of increased elitism.

The Pendleton Act

“…The act established that employees in certain “classified” positions would need to be selected through open, competitive and anonymously graded exams (United States Civil Service Commission, 1883). The act divided the classified (that is, subject to exams) civil service into three branches: the “classified departmental service” for employees in the executive departments in DC, the “classified Customs Service” for Customs Service employees, and the “classified Postal Service” for postal workers. Figure 1 shows the total number of workers in the Executive Departments in DC, as well as the share who worked in positions that would be covered by exams after 1883. Here, we define a position as “covered by exams” if it became part of the classified service at any point between 1883 and 1893. The total number of employees grew in the decade prior to the Pendleton Act, reflecting the expansion of government functions (Libecap & Johnson, 2007). Note that, since our analysis includes office fixed effects, offices created after the reform do not contribute to the identification of the effects of exams.17 Growth in the number of employees seems to level off after 1883, both in exempted and non-exempted positions (as reflected by the stability of the share of workers in exempted positions, which fluctuated around 60% throughout the period)….”

Measuring Employees’ Social and Professional Backgrounds

“…We collected information on employees’ socioeconomic backgrounds by linking the Registers to US population censuses, using name-based matching techniques (Abramitzky et al., 2019). Specifically, we used workers’ names, birthplaces and approximate ages to link each of the 1871-1893 Registers to the 1850, 1860, 1870 and 1880 censuses. Through this procedure, we obtained information about: (1) employees’ family backgrounds, including characteristics such as race, parental wealth and parental occupations; and (2) employees’ occupations prior to joining the federal government. We provide further details on the linking strategy and sensitivity checks in Online Appendix Section A. However, we note here that: (1) employees hired through exams are not more (or less) likely to be matched to the census (Table A1), (2) the results that do not require the linked data are very similar when estimated in this linked sample (Table A2), and (3) the results are similar when we reweight the sample to account for differences in the matching probability across different individuals (Table A3)…”

Core finding

“…Our identification strategy exploits the fact that not all federal positions were initially subject to exams. Specifically, among positions in the Executive Departments in DC, the reform exempted those at the bottom (such as laborers) and at the top (such as bureau chiefs) of the state hierarchy. We use this feature of the reform to estimate a difference-in-differences model, comparing the characteristics of employees hired before and after the reform, in exempted and non-exempted positions. In other words, we ask if individuals hired to do the same job in the same office were of a different social background when hired through exams rather than through patronage. We find that the reform led to an immediate decline in the share of employees from disadvantaged backgrounds, which then persisted for at least 10 years after its implementation. First, employees hired through exams came from families that were 6.5 percentile ranks higher in the national wealth distribution. This increase was driven by a reduced representation of applicants with parents at the bottom of the distribution, together with an increase in the share of workers from middle- and upper-middle class families. Second, the reform increased the share of employees with higher-status parental occupations: we find a 5 percentage points increase in the proportion of children of professionals (a nearly 50% increase), together with a similar decrease in the pro-portion of children of blue-collar workers. Finally, the reform led to a 4 percentage points decline in the share of foreign-born employees, as well as a 7 percentage points decline in the share of employees with foreign-born parents. Interestingly, this increased elitism occurred despite the exam was based on content that should have in principle been accessible for applicants with only a “common school” education. Why did the introduction of exams improve the prospects of middle-class applicants (at the expense of applicants from disadvantaged backgrounds)? Our interpretation is that, by increasing the relative weight of formal education in hiring decisions, the reform helped “educated outsiders”: individuals with high levels of education but low levels of connections. As middle-class applicants were overrepresented in this group, the reform increased their chances of obtaining a government job….”

The Evidence

“…We present three pieces of evidence that support this interpretation. First, we show that the reform indeed increased the representation of “educated outsiders”. In particular, employees hired through exams were 10 percentage points more likely to have held a professional occupation - such as lawyers and accountants- prior to joining government than patronage employees.7 Exam based hires were also more likely to lack the personal and political connections that would have facilitated access to patronage jobs: they were less likely to have a father who was himself a bureaucrat, less likely to have grown up in DC, and less likely to come from a county in which a majority of voters supported the incumbent party (suggesting a decline in political favoritism). Second, we document that middle-class individuals were likely overrepresented among the “educated outsiders": Although middle-class applicants were more educated than those from poorer backgrounds, in the pre-reform period they represented a similarly small fraction of workers in the federal positions that would become subject to exams. Moreover, after the reform, the representation of middle-class individuals in these positions became closer to their representation in comparable private sector white-collar jobs. Third, we show that exams had the most negative effects on the chances of applicants from disadvantaged backgrounds when applicants came from states with high levels of inequality in access to schooling- presumably the places in which the children of the poor were the least represented among “educated outsiders”. We discuss two additional explanations for the reduced representation of applicants from disadvantaged backgrounds. First, the reform mandated exams to be held throughout the country, not just in Washington, DC. This expanded geographical coverage could have per se affected the selection of candidates. However, the increase in employees’ social status remains even when we restrict the comparison to employees who grew up in the same location. Second, exam-based recruitment might have gradually changed the appeal of government jobs, thus changing the pool of individuals interested in such positions. Although this effect is inherent to any move towards “meritocracy” (and the combined effect of changes in screening and changes in the applicant pool is still policy-relevant), we find evidence that such change in preferences is unlikely to drive of our findings: we observe a rapid change in bureaucrats’ social backgrounds, which seems inconsistent with plausibly slower to change perceptions about the prestige of public employment….”

“…Employees hired through examinations had higher average parental wealth than those hired through patronage. Such increases could be compatible with increases in the representation of the middle class at the expense of the children of the poor, or with increases in the representation of the upper class at the expense of the middle (or by some combination of the two). To investigate which groups increased and which groups decreased their representation as a result of the reform, we split individuals based on the wealth quintile of their parents. Panels (a) and (b) in Figure 4 show, for employees in positions that became subject to exams after 1883, their distribution across family wealth quintiles in the pre- and post-reform periods. Panels (c) and (d) show the same distribution but for employees in positions that did not become subject to exams. Among workers in positions subject to exams, workers from the top quintile were overrepresented prior to the reform. Specifically, individuals from the top 20% accounted for about 35% of workers in such positions. However, there were no large differences in the relative representation of individuals from the bottom four quintiles: each of these groups accounted for about 15% of workers prior to the reform. After the reform, in contrast, we observe a sharp increase (from 15 to 25%) in the proportion of workers from the 60-80 quintile. This increase seems to come mostly at the expense of workers in the bottom two quintiles, with more limited changes in the share of workers from the top quintile. Among those in positions exempted from exams, both the top and the bottom family wealth quintiles were overrepresented in the pre-reform period (Panel (c)). This bimodal distribution likely reflects the fact that exempted positions included highly-paid leadership positions (such as bureau chiefs) as well as low-paid positions (such as laborers). However, this overrepresentation of both the bottom and the top of the parental wealth distribution appears to continue in the postreform period (Panel (d)). Panel (e) in Figure 4 confirms this pattern when we run separate regressions in which the dependent variables are indicators for belonging to the different quintiles of the parental wealth distribution. First, we find no change in the likelihood that an employee would belong to the top 20%. Second, we find an increased representation of the middle and upper middle class (that is, families between the 40 and the 80 percentiles of the wealth distribution), which comes at the expense of a reduced representation of families in the bottom two quintiles (particularly the bottom 20%)….”

“…Summary of Results. Our findings indicate that workers appointed through exams were of a higher social class than those appointed through patronage: they had higher levels of parental wealth, were more likely to be the children of professionals, and were less likely to be the children of immigrants (or immigrants themselves). This increase in social status occurred immediately after the passing of the reform and persisted at least 10 years after its implementation..”

Diana Moreira and Santiago Pérez, "Who Benefits from Meritocracy?,"National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30113

"... An audit released on June 16 of the Illinois Department of Employment Security found that half of federal Pandemic Unemployment Assistance (PUA) benefits the agency paid out during the height of the pandemic were stolen. The report by Illinois’ auditor general states that the Illinois agency, which administers both state and federal unemployment benefits, paid $3.6 billion in federal PUA benefits during state fiscal year 2021. The report describes “overpayments associated with ID theft and traditional fraud within the PUA program” as “unprecedented.” Specifically, the audit states that ID theft overpayments under the PUA program totaled over $1.8 billion—or 50.4 percent of the $3.6 billion in PUA payments...."

Matt Weidinger, "Audit Finds Half of Pandemic Unemployment Assistance Benefits in Illinois Were Stolen,"American Enterprise Institute, June 23, 2022, https://www.aei.org/poverty-studies/audit-finds-half-of-pandemic-unemployment-assistance-benefits-in-illinois-were-stolen/

Audit Finds Half of Pandemic Unemployment Assistance Benefits in Illinois Were Stolen

An audit released on June 16 of the Illinois Department of Employment Security found that half of federal Pandemic Unemployment Assistance (PUA) benefits the agency paid out during the height of the pandemic were stolen.

The report by Illinois’ auditor general states that the Illinois agency, which administers both state and federal unemployment benefits, paid $3.6 billion in federal PUA benefits during state fiscal year 2021. The report describes “overpayments associated with ID theft and traditional fraud within the PUA program” as “unprecedented.” Specifically, the audit states that ID theft overpayments under the PUA program totaled over $1.8 billion—or 50.4 percent of the $3.6 billion in PUA payments.

Federal officials regularly confirm that the temporary and now-expired PUA program was uniquely subject to fraud and abuse. US Department of Labor Inspector General (IG) Larry Turner noted in his March 2022 Congressional testimony that PUA “posed the greatest risk to the UI system,” continuing warnings the IG issued about the program starting in May 2020, just weeks after its creation. Turner stated the elevated fraud risk was driven by “PUA’s expanded coverage” to groups not traditionally eligible for unemployment benefits, as well as the program’s allowing claimants to “self-certify their eligibility” for benefits. Earlier this month, the nonpartisan Government Accountability Office similarly raised concerns over the self-certification process used by PUA, while arguing the unemployment benefits system overall remains at “high risk” for fraud and abuse.

As state reports like this start to emerge, official national estimates of PUA misspending remain unavailable. Turner’s testimony qualified his estimate of a shocking $163 billion in misspending across all pandemic unemployment benefit programs by adding that the real total is “likely higher” since that figure does not include elevated abuse under the PUA program. Counting such elevated abuse, outside experts have estimated pandemic unemployment benefit losses to fraud could total as high as $400 billion. For comparison, all benefits paid out under the nation’s unemployment benefits system in calendar year 2019 totaled under $30 billion.

News accounts suggest the Illinois Department of Employment Security has blamed “insufficient and flawed federal guidance” as well as “a hastily constructed program put together by the Trump administration” for their extraordinary PUA misspending. They are right about the “hastily constructed” part, but that ignores the reality that the PUA program was designed by senior Democratic lawmakers. On March 12, 2020, current Senate Majority Leader Chuck Schumer (D-NY) and Finance Committee Chairman Ron Wyden (D-OR) introduced the “Pandemic Unemployment Assistance Act,” which proposed creating the unprecedented PUA program. That legislation was subsequently included in the massive CARES Act President Donald Trump signed into law two weeks later on March 27, 2020. Upon Senate passage, Schumer stated he “conceived” of the unemployment benefit expansions in the new law, which he dubbed “unemployment insurance on steroids.” Those expansions included the unprecedented benefits for “the self-employed and workers in the gig economy” offered by the PUA program.

A review of the legislative text reveals that the Schumer/Wyden bill is almost identical to the eventual CARES Act PUA policy. Indeed, the only major difference involves the proposed duration of the PUA program. Under the CARES Act, PUA was originally authorized through December 26, 2020, and subsequent extensions continued the temporary program until it expired nationwide on Labor Day 2021. Under the Schumer/Wyden bill, PUA would have operated until six months after the COVID-19 public health emergency expired; the most recent renewal of that determination was issued in April 2022. That means that, under the Schumer/Wyden bill, PUA—and presumably its extraordinary losses to fraud—would still be continuing even today.

I quantify and track the impact of supply- and demand-related factors on personal consumption expenditures (PCE) inflation. I assess inflation rates by spending category. I divide categories in the PCE basket into supply- and demand-driven groups. Demand-driven categories are identified as those where an unexpected change in price moves in the same direction as the unexpected change in quantity in a given month; supply-driven categories are identified as those where unexpected changes in price and quantity move in opposite directions. This methodology accounts for the evolving impact of supply- versus demand-driven factors on inflation from month to month. My analysis highlights that both supply and demand factors are responsible for current elevated inflation levels. Supply factors explain about half of the difference between current 12-month PCE inflation and pre-pandemic inflation levels, and the effects appear to be rising more recently. Demand factors are responsible for about a third of the difference, and those effects appear to be diminishing more recently. The remainder is due to factors that cannot be definitively labeled as supply or demand.The large impact of supply factors implies that inflationary pressures will not completely subside until labor shortages, production constraints, and shipping delays are resolved. Although supply disruptions are widely expected to ease this year, this outcome is highly uncertain. Figure 1 shows the contributions to 12-month headline PCE inflation from supply- (green) and demand-driven (blue) inflation between 1998 and 2022. Yellow sections show the ambiguous portion of PCE inflation that is not labeled as either demand or supply driven. I calculate the monthly contributions to inflation by multiplying the change in price for a given category by its most recent respective weight in the PCE index. The monthly contributions for demand-driven, supply-driven, and ambiguous categories are then calculated as the sum of contributions from all items within that category. The contributions for demand-driven and supply-driven inflation are then calculated as the 12-month trailing sum of their respective monthly contributions. This generates a series that can be directly compared to the year-over-year PCE inflation rate. Figure 2 compares the most recent contributions of supply-driven inflation in panel A and demand-driven inflation in panel B against their respective 2010-2019 averages.Supply-driven inflation is currently contributing 2.5 percentage points (pp) more than its pre-pandemic average, while demand-driven inflation is currently contributing 1.4pp more. Thus, supply-driven inflation explains a little more than half of the 4.8pp gap between current levels of year-over-year PCE inflation and its pre-pandemic average level.Demand factors explain a smaller share of elevated inflation levels, accounting for about one-third of the difference. The ambiguous category, which is not shown, explains the remainder of the difference.Figure 3 breaks down these contributions over past five years, showing the one-month changes used to construct the 12-month trailing sums shown in Figure 1.

Adam Hale Shapiro, "How Much Do Supply and Demand Drive Inflation?,"Federal Reserve Board Of San Francisco, June 21, 2022, https://www.frbsf.org/economic-research/publications/economic-letter/2022/june/how-much-do-supply-and-demand-drive-inflation/

How Much Do Supply and Demand Drive Inflation?

Inflation declined rapidly at the onset of the pandemic in the spring of 2020 before taking a dramatic turn upward in early 2021, rising to levels that remain well above the Federal Reserve’s longer-run goal of 2% on average. Researchers and policymakers have pointed to both supply and demand factors as being responsible for elevated inflation. For instance, Barnichon and Shapiro (2022) showed the impact of supply-related factors such as labor shortages, while Barnichon, Oliveira, and Shapiro (2021) and Jordà et al. (2022) demonstrated the importance of heightened demand stemming from pandemic-related fiscal relief. The extent to which either supply or demand factors are responsible for higher inflation levels has important implications for monetary policy. As Fed Chair Powell stated in a recent interview, “What can control is demand, we can’t really affect supply with our policies…so the question whether we can execute a soft landing or not, it may actually depend on factors that we don’t control” (Marketplace 2022).

In this Economic Letter, I quantify and track the impact of supply- and demand-related factors on personal consumption expenditures (PCE) inflation. Similar to the methodology introduced in Mahedy and Shapiro (2017), and outlined in Shapiro (2022), I assess inflation rates by spending category. I divide categories in the PCE basket into supply- and demand-driven groups. Demand-driven categories are identified as those where an unexpected change in price moves in the same direction as the unexpected change in quantity in a given month; supply-driven categories are identified as those where unexpected changes in price and quantity move in opposite directions. This methodology accounts for the evolving impact of supply- versus demand-driven factors on inflation from month to month. To help monitor these changes, the San Francisco Fed has launched a new Supply- and Demand-Driven PCE Inflation data page with monthly data updates.

My analysis highlights that both supply and demand factors are responsible for current elevated inflation levels. Supply factors explain about half of the difference between current 12-month PCE inflation and pre-pandemic inflation levels, and the effects appear to be rising more recently. Demand factors are responsible for about a third of the difference, and those effects appear to be diminishing more recently. The remainder is due to factors that cannot be definitively labeled as supply or demand. The large impact of supply factors implies that inflationary pressures will not completely subside until labor shortages, production constraints, and shipping delays are resolved. Although supply disruptions are widely expected to ease this year, this outcome is highly uncertain.

Separating supply and demand drivers

This analysis uses the more than 100 goods and services categories in the PCE index. For each month of data, I separate the categories where prices moved due to a change in demand from those where prices moved due to a change in supply. To do so, I rely on simple microeconomic theory: Shifts in demand move both prices and quantities in the same direction along the upward-sloping supply curve, meaning prices rise as demand increases. Shifts in supply move prices and quantities in opposite directions along the downward-sloping demand curve, meaning prices rise when supplies decline.

Each month I estimate the changes in the price level and quantity level of each category. It is important to isolate the unexpected components of the changes in prices and quantities, as opposed to the simple change itself. This is because prices and quantities generally continue an existing trend. These expected trend components are not likely to represent a shift in demand or supply, but instead reflect longer-run factors such as technological improvements, cost-of-living adjustments to wages, or demographic changes like population aging.

Extracting the unexpected components of the monthly changes in price and quantity for each category is an iterative process. I run 10-year-window rolling regressions for both price and quantity. For example, the first window begins in January 1988, the first period PCE data are available at the detailed level, and ends in December 1997. This generates predicted values for price and quantity in January 1998, which I then compare to the actual values of price and quantity in that same month. If the actual values of price and quantity are both above or both below their predicted values, the category is labeled as “demand-driven” in January 1998. If the difference between the actual and predicted values are of opposite signs, the category is labeled as “supply-driven” in that month. If either of the actual values is close enough to its predicted value that the difference is statistically indistinguishable from zero, the category is labeled as “ambiguous” in that month. I then roll the data window forward one month and repeat the process. I iterate this process for each month until I reach the last window of data, which for this Letter begins in May 2012 and ends in April 2022.

Categories that experience frequent supply-driven price changes include food and household products such as dishes, linens, and household paper items. Categories that experience frequent demand-driven price changes include motor vehicle-related products, used cars, and electricity. However, assessing the past two years of data reveals some changes in which categories experienced demand and supply shocks in the post-pandemic period. Specifically, categories with extraordinarily frequent supply-driven price changes in 2021 and 2022 include products with known supply constraints during the pandemic, such as new automobiles, fuel, and repair services. Categories with extraordinarily frequent demand-driven price changes during this period include many goods consumed at home—for example, furniture, clothing, toys, video equipment, and cookware—as well as services related to reopening from pandemic-related closures, such as restaurants and museums.

Figure 1 shows the contributions to 12-month headline PCE inflation from supply- (green) and demand-driven (blue) inflation between 1998 and 2022. Yellow sections show the ambiguous portion of PCE inflation that is not labeled as either demand or supply driven. I calculate the monthly contributions to inflation by multiplying the change in price for a given category by its most recent respective weight in the PCE index. The monthly contributions for demand-driven, supply-driven, and ambiguous categories are then calculated as the sum of contributions from all items within that category. The contributions for demand-driven and supply-driven inflation are then calculated as the 12-month trailing sum of their respective monthly contributions. This generates a series that can be directly compared to the year-over-year PCE inflation rate.

Inflation drivers over the pandemic period

To get a sense of whether supply or demand factors are responsible for current elevated inflation levels, I compare current supply- and demand-driven inflation to their average levels from the 10 years before the pandemic. During the pre-pandemic period, PCE inflation averaged 1.5%, considerably below the April 2022 rate of 6.3%. Figure 2 compares the most recent contributions of supply-driven inflation in panel A and demand-driven inflation in panel B against their respective 2010-2019 averages. Supply-driven inflation is currently contributing 2.5 percentage points (pp) more than its pre-pandemic average, while demand-driven inflation is currently contributing 1.4pp more. Thus, supply-driven inflation explains a little more than half of the 4.8pp gap between current levels of year-over-year PCE inflation and its pre-pandemic average level. Demand factors explain a smaller share of elevated inflation levels, accounting for about one-third of the difference. The ambiguous category, which is not shown, explains the remainder of the difference.

Finally, I assess the contributions to annualized monthly changes in headline PCE inflation. This provides a higher frequency depiction of how supply and demand factors affected inflation over the pandemic period. Figure 3 breaks down these contributions over past five years, showing the one-month changes used to construct the 12-month trailing sums shown in Figure 1.

Meanwhile, supply factors began to arise in April 2021, indicating a slightly delayed response from the economy reopening. Supply-driven inflation has remained elevated since then and has accelerated more recently. This acceleration is attributable to food and energy supply disruptions, including those associated with the invasion of Ukraine.

Conclusion

Analysis in this Letter shows that supply factors are responsible for more than half of the current elevated level of 12-month PCE inflation. This in part reflects supply constraints from continued labor shortages and global supply disruptions related to the pandemic and the war in Ukraine. While demand factors played a large role in the spring of 2021, they explain only about a third of recent elevated inflation levels. Factors that cannot be labeled as either demand or supply are also playing a nontrivial role.

These results showing that factors other than demand account for about two-thirds of recent elevated inflation highlight some risks for the economy. Because supply shocks raise prices and suppress economic activity, the prevalence of supply-related factors raises the risk of entering a period of low growth and elevated inflation levels. This risk depends crucially on how long labor shortages and global supply disruptions persist. While supply disruptions are widely expected to ease this year, this outcome is highly uncertain.

References

Jordà, Òscar, Celeste Liu, Fernanda Nechio, and Fabian Rivera-Reyes. 2022. “Why Is U.S. Inflation Higher than in Other Countries?” FRBSF Economic Letter 2022-07 (March 28).

Barnichon, Regis, Luiz E. Oliveira, and Adam Hale Shapiro. 2021. “Is the American Rescue Plan Taking Us Back to the’60s?” FRBSF Economic Letter 2021-27 (October 18).

Barnichon, Regis, and Adam Hale Shapiro. 2022. “What’s the Best Measure of Economic Slack?” FRBSF Economic Letter 2022-04 (February 22).

Mahedy, Tim, and Adam Hale Shapiro. 2017. “What’s Down with Inflation?” FRBSF Economic Letter 2017-35 (November 27).

Powell, Jerome. 2022. “Inflation, Soft Landings and the Federal Reserve.” Interview by Kai Ryssdal, Marketplace Business News Podcast, NPR, May 12. Audio, 27:38.

Shapiro, Adam Hale. 2022. “A Simple Framework to Monitor Inflation.” FRB San Francisco Working Paper 2020-29.

Our baseline forecast assumes that the fading effects of fiscal transfers and pandemic bonuses will lower wage growth by 0.75pp; inflationary shocks will gradually diminish, bringing down short-term inflation expectations by enough to slow wage growth by another 0.5pp; 1 million workers will return to the labor force, slowing wage growth by another 0.2pp; and the job openings rate will fall 0.5pp due to natural normalization, reducing wage growth by just over 0.1pp (Exhibit 9). To return wage growth the rest of the way to 3.75%, policymakers would need to further reduce labor demand by enough to lower the job openings rate by another 1pp and raise the unemployment rate by 0.3pp. We previously estimated that this would require GDP growth of about 1-1.5%, below potential but not in recessionary territory. The labor force participation rate remains depressed by 1.1pp relative to its pre-pandemic rate, equivalent to a 2.85mn reduction in the labor force. We estimate that 700k of this is due to natural population aging, 850k is due to early retirements that are less likely to reverse, and 1.3mn is due to a combination of other reasons including Covid fears, a larger financial cushion due to pandemic savings and house and stock price appreciation, and changes in lifestyle (Exhibit 5). Our baseline forecast assumes that 1mn people will return to the labor force in the next year, including a modest share of the early retirees and most of the others who left. Encouragingly, most prime-age workers who left the labor force during the pandemic still intend to search for work in the next 12 months (Exhibit 6).

Jan Hatzius, Alec Phillips, David Mericle, Spencer Hill, Joseph Briggs, Ronnie Walker and Manuel Abecasis, “Prospects For A Soft Landing: What Could Make The Fed’s Job Easier Or Harder,” Goldman Sachs, June 5, 2022

We use the price effects caused by the passage of rent control in St. Paul, Minnesota in 2021, to study the transfer of wealth across income groups. First, we find that rent control caused property values to fall by 6-7%, for an aggregate loss of $1.6 billion.A calibrated model of house prices under rent control attributes a third of these losses to indirect, negative externalities. Second, leveraging administrative parcel-level data, we find that the tenants who gained the most from rent control had higher incomes and were more likely to be white, while the owners who lost the most had lower incomes and were more likely to be minorities. For properties with high-income owners and low-income tenants, the transfer of wealth was close to zero. Thus, to the extent that rent control is intended to transfer wealth from high-income to low-income households, the realized impact of the law was the opposite of its intention.Table IV presents estimates of Equation 3 using data from the Twin Cities area that control for building size, age, type (multi-family, single-family, etc.), year-month fixed effects, and geographic location fixed effects. Throughout the paper, standard errors are double-clustered by year-month and by the geographic level of the fixed effects. Across the three specifications, we find that rent control caused a statistically significant decline in transaction prices of about 6%. This estimate does not vary across the three types of geographic fixed effects. Additionally, the magnitude of the results is similar to the univariate estimates. This suggests that the negative effect of rent control is not confounded by location or the traits of the properties. In Internet Appendix Table III, we find similar results when the control sample is restricted to the cities neighboring St. Paul, with prices falling between 4.5% and 5% in St. Paul following the reform. The slightly muted response in the closer cities is consistent with a spillover effect from St. Paul onto neighboring areas

Kenneth Ahern and Marco Giacoletti, "Robbing Peter to Pay Paul? The Redistribution of Wealth Caused by Rent Control,"National Bureau Of Economic Research, May 2022, https://www.nber.org/papers/w30083

Core Findings

“…Using a sample of nearly 150,000 real estate transactions over the period January 2018 to January 2022 in the five counties surrounding St. Paul, we first estimate the effect of St. Paul’s rent control on property values. These difference-in-difference tests identify the change in transaction prices of residential real estate in St. Paul following the passage of rent control, relative to the change in prices during the same period in cities adjacent to St. Paul. The tests control for i) year-month fixed effects to absorb common time-series variation in prices in the St. Paul area, ii) detailed location fixed effects to absorb time-invariant cross-sectional variation in prices across granular geographic regions, and iii) property-level attributes, including building age and size, and whether the property is a multi-unit or single-family residence. We find that the introduction of rent control caused an economically and statistically significant decline of 6-7% in the value of real estate in St. Paul. Because we control for year-month fixed effects, these results do not reflect seasonal changes caused by declining volume in winter months. Second, we run additional tests to account for changing preferences for suburbs over city centers using transactions from five Midwestern cities comparable to St. Paul. In triple-differences models that exploit variation in city centers versus suburbs, we still find that rent control caused a 6- 8% decline in property values. Third, we find that rental properties in St. Paul experienced an additional 6% decline in value compared to owner-occupied properties in St. Paul, for a total loss of about 12%. These results imply that the value loss is caused by rent control rather than a spurious variable that affected rentals and non-rentals equally. Finally, we verify that our results are unlikely to be caused by selection bias….”

“…To decompose the observed value loss into direct capitalization effects and indirect negative externalities, we derive a simple model of rent control that allows for stochastic growth rates and probabilistic transitions between owner-occupied and rental housing. Matching the model’s parameters to the St. Paul market, we estimate that about two-thirds of the value loss is driven by capitalization effects and one-third is driven by externalities. These results suggest that capitalization effects of rent control can have a large impact on prices even for owner-occupied properties with a small likelihood of switching to the rental market. The large decline in property values caused by rent control has significant consequences for St. Paul’s economy. Assuming that the market transactions we observe represent the average residential property in St. Paul, rent control would have caused an aggregated loss of $1.57 billion in property value and a 4% expected shortfall in property tax revenue. Given that property taxes are the main form of revenue for the city and the school district, the shortfall in tax revenue is likely to lead to tax increases to maintain city services. Next, we investigate our second research question: how does rent control redistribute wealth? The intention of St. Paul’s rent control is to reduce the burden of housing costs for low-income renters. To study whether the law achieved its intended goal, we test whether the wealth transfers caused by the law are larger when owners have higher incomes and renters have lower incomes….”

“…Next, we use a hedonic pricing model to predict the change in property value following rent control for over 60,000 residential parcels in St. Paul. Due to limitations in the administrative data, we focus on properties with three or fewer units owned by small landlords. This accounts for 90% of rental properties and 54% of all rental housing units in St. Paul. The large majority of residential parcels in St. Paul are single-family residences (89% of all parcels), of which 17% are rental properties. Of the rental properties in our sample, 43% are owned by small landlords. To measure the traits of renters and owners, we use highly granular Census data. We proxy for the traits of renters based on Census data corresponding to the property address. To proxy for the traits of owners, we collect their addresses from the county assessor’s office. To verify that the address of the owner is residential, rather than commercial, we match owners’ addresses to the US Postal Service’s residential delivery indicator (RDI). We then classify rental properties owners as small landlords if their listed address is residential and different than the property address and as large landlords if their listed address is commercial. For small landlords, we proxy for their demographic traits using the granular Census data that correspond with their home address. To test whether transfers are larger when renters have lower incomes and owners have higher incomes, we create a ‘high disparity’ subsample of properties in which owners have incomes above the median-owner’s income and renters have incomes below the median-renter’s income. We also create a ‘low disparity’ subsample in which owners have below-median income and renters have above-median income. The differences between the two subsamples are stark. In the high disparity subsample, the median owner’s income is more than double the income of the median renter, while in the low disparity subsample, owners’ and renters’ median incomes are statistically equivalent. Likewise, the fraction of minority renters is roughly 50% in the high disparity subsample, compared to 25% in the low disparity subsample. Similar variation exists for age and education. In contrast to the stated goals of the rent control law, we find that the largest transfer of wealth occurred in the low disparity subsample (8.52%) in which renters are relatively wealthier, while the smallest transfer occurred in the high disparity subsample (0.89%), in which renters are relatively poorer. This pattern persists in cross-sectional regressions. Wealth transfers are positively related to renters’ income and negatively related to owner’s income.

“…The MoM increase in Case-Shiller was at 2.09%. This is the largest MoM increase in the Case-Shiller index on record (since 1975), and stronger than any month during the housing bubble or during the late ‘70s with high inflation. In March, the months-of-supply was at 1.9 months, and the Case-Shiller National Index (SA) increased 2.09% month-over-month. The black arrow points to the March 2022 dot. In the April existing home sales report, the NAR reported months-of-supply increased to 2.2 months. This month was very likely the peak YoY growth rate - just above the peak last August. Since inventory is now increasing year-over-year (but still low), we should expect price increases to slow. The normal level of inventory is probably in the 4 to 6 months range, and we will have to see a significant increase in inventory to sharply slow price increases, and that is why I’m focused on inventory! Since Case-Shiller is a 3-month average, and this report was for March (includes January and February), this included price increases when mortgage rates were significantly lower than today. In January, the Freddie Mac PMMS averaged 3.4% for a 30-year mortgage, and 3.8% in February. Currently mortgage rates are around 5.25%....”

Bill McBride, "Case-Shiller National Index up 20.6% Year-over-year in March; New Record Monthly Increase,"Calculated Risk, May 31, 2022, https://calculatedrisk.substack.com/p/case-shiller-national-index-up-206

Case-Shiller National Index up 20.6% Year-over-year in March; New Record Monthly Increase

Both the Case-Shiller House Price Index (HPI) and the Federal Housing Finance Agency (FHFA) HPI for March were released today. Here is a graph of the month-over-month (MoM) change in the Case-Shiller National Index Seasonally Adjusted (SA).

The Case-Shiller Home Price Indices for “March” is a 3-month average of January, February and March prices.

FHFA House Price Index

On the FHFA index: U.S. House Prices Rise 18.7 Percent over the Last Year; Up 4.6 Percent from the Fourth Quarter

U.S. house prices rose 18.7 percent from the first quarter of 2021 to the first quarter of 2022 according to the Federal Housing Finance Agency House Price Index (FHFA HPI®). House prices were up 4.6 percent compared to the fourth quarter of 2021. FHFA’s seasonally adjusted monthly index for March was up 1.5 percent from February.

"High appreciation rates continued across housing markets during the first quarter of 2022," said William Doerner, Ph.D., Supervisory Economist in FHFA's Division of Research and Statistics. "Strong demand coupled with tight supply have kept prices climbing. Through the end of March, higher mortgage rates have not yet translated into slower price gains, but new home sales have dropped during the last few months, with a significant falloff in April."
emphasis added
This is the monthly and quarterly indexes. Here is a graph from the FHFA report showing the annual change by region for March 2022 compared to March 2021. Prices have increased sharply everywhere, but especially in the Mountain, South Atlantic, East South Central and Pacific regions.

From S&P: S&P Corelogic Case-Shiller Index Reports Annual Home Price Gain Of 20.6% In March

the S&P CoreLogic Case-Shiller U.S. National Home Price NSA Index, covering all nine U.S. census divisions, reported a 20.6% annual gain in March, up from 20.0% in the previous month. The 10-City Composite annual increase came in at 19.5%, up from 18.7% in the previous month. The 20-City Composite posted a 21.2% year-over-year gain, up from 20.3% in the previous month.

Tampa, Phoenix, and Miami reported the highest year-over-year gains among the 20 cities in March. Tampa led the way with a 34.8% year-over-year price increase, followed by Phoenix with a 32.4% increase, and Miami with a 32.0% increase. Seventeen of the 20 cities reported higher price increases in the year ending March 2022 versus the year ending February 2022.
...
“Those of us who have been anticipating a deceleration in the growth rate of U.S. home prices will have to wait at least a month longer,” says Craig J. Lazzara, Managing Director at S&P DJI. “The National Composite Index recorded a gain of 20.6% for the 12 months ended March 2022; the 10- and 20-City Composites rose 19.5% and 21.2%, respectively.For both National and 20-City Composites, March’s reading was the highest year-over-year price change in more than 35 years of data, with the 10-City growth rate at the 99th percentile of its own history.

“The strength of the Composite indices suggests very broad strength in the housing market, which we continue to observe. All 20 cities saw double-digit price increases for the 12 months ended in March, and price growth in 17 cities accelerated relative to February’s report. March’s price increase ranked in the top quintile of historical experience for every city, and in the top decile for 19 of them. emphasis added
This graph shows the nominal seasonally adjusted Composite 10, Composite 20 and National indices (the Composite 20 was started in January 2000).

House Prices and Inventory

This graph below shows existing home months-of-supply (inverted, from the NAR) vs. the seasonally adjusted month-to-month price change in the Case-Shiller National Index (both since January 1999 through March 2022).

Note that the months-of-supply is not seasonally adjusted.

In March, the months-of-supply was at 1.9 months, and the Case-Shiller National Index (SA) increased 2.09% month-over-month. The black arrow points to the March 2022 dot. In the April existing home sales report, the NAR reported months-of-supply increased to 2.2 months.

This month was very likely the peak YoY growth rate - just above the peak last August. Since inventory is now increasing year-over-year (but still low), we should expect price increases to slow.

The normal level of inventory is probably in the 4 to 6 months range, and we will have to see a significant increase in inventory to sharply slow price increases, and that is why I’m focused on inventory!

Since Case-Shiller is a 3-month average, and this report was for March (includes January and February), this included price increases when mortgage rates were significantly lower than today. In January, the Freddie Mac PMMS averaged 3.4% for a 30-year mortgage, and 3.8% in February. Currently mortgage rates are around 5.25%.

Note: I’ll have more on real prices, price-to-rent and affordability tomorrow.

“…The truth is, an analysis of the 10 best-known, widely cited pre-K programs makes clear that the research around these programs shows not “that ‘pre-K works’” but that “some early childhood programs yield particular outcomes, sometimes, for some children.” Indeed, the most credible research has made clear that “the most meaningful, far-reaching effects” are the product of “carefully designed, well-implemented programs”—not sweeping mandates. And even the studies that do indicate pre-K efforts were beneficial don’t necessarily point at a clear path forward for policies on pre-K. Consider the famed Perry Preschool project, a small pilot preschool and home-visiting program for 3- and 4-year-olds that ran from 1962 to 1967 in Ypsilanti, Michigan. The program provided 58 low-income Black children with two years of a research-based preschool curriculum and weekly home visits. It was found to have positive impacts on participants’ educational and life outcomes, reducing teenage pregnancy, and increasing lifetime earnings. All good stuff. But we’re talking about less than 60 students, in an intensive boutique pilot program, more than a half-century ago. The notion that Perry offers a clear blueprint for broad public policies is just fantastic….”

Frederick Hess, "The Not-So-Certain Science of Pre-K,"American Enterprise Institute, May 25, 2022, https://www.aei.org/op-eds/the-not-so-certain-science-of-pre-k/

The Not-So-Certain Science of Pre-K

There’s a lot to like about preschool. Over the years, my kids have used several different preschools—from a cheerful church basement to a hard-core Montessori—and had a number of really positive experiences. You don’t need to convince me that good, affordable, reliable, and convenient preschool is something we should value.

But it’s a big leap from there to concluding that preschool “works” or that universal pre-K is a good idea. Now, when I offer such qualifications, preschool aficionados immediately leap to patiently (or not-so-patiently) explain that I’m ignoring the science.

As for that science. Earlier this year, researchers at Vanderbilt University released the outcomes of a new gold-standard study of Tennessee’s statewide pre-K program, launched back in 2009. The study has been touted as the only randomized “thorough, ongoing investigation into the impacts of a statewide pre-K program for economically disadvantaged children.” The findings of the initial 2015 report were regarded as highly disappointing, with the benefits of the pre-K program fading before 1st grade. The follow-up results aren’t any better. The pre-K students fared worse on state assessments than their peers and had worse outcomes “for disciplinary infractions, attendance, and receipt of special education services.”

Readers who regularly hear that pre-K “works” might be surprised. They shouldn’t be. The truth is, an analysis of the 10 best-known, widely cited pre-K programs makes clear that the research around these programs shows not “that ‘pre-K works’” but that “some early childhood programs yield particular outcomes, sometimes, for some children.” Indeed, the most credible research has made clear that “the most meaningful, far-reaching effects” are the product of “carefully designed, well-implemented programs”—not sweeping mandates.

And even the studies that do indicate pre-K efforts were beneficial don’t necessarily point at a clear path forward for policies on pre-K. Consider the famed Perry Preschool project, a small pilot preschool and home-visiting program for 3- and 4-year-olds that ran from 1962 to 1967 in Ypsilanti, Michigan. The program provided 58 low-income Black children with two years of a research-based preschool curriculum and weekly home visits. It was found to have positive impacts on participants’ educational and life outcomes, reducing teenage pregnancy, and increasing lifetime earnings. All good stuff. But we’re talking about less than 60 students, in an intensive boutique pilot program, more than a half-century ago. The notion that Perry offers a clear blueprint for broad public policies is just fantastic.

And yet, some years back, Nobel Prize-winning economist James Heckman savaged a newspaper columnist for even questioning the benefits of expanded pre-K. Heckman insisted that it clearly works. How did he know this? Heckman pointed to his “evidence-based analysis of more than 30 years of data” from the Perry Preschool project. “It is as good a trial for effectiveness as those we currently rely on to evaluate prescription and over-the-counter drugs,” he wrote.

While Heckman is a very smart guy, he was wrong here. There’s a difference between a medical therapeutic and a public-policy response. Unlike a preschool program, the ingredients of an over-the-counter drug can be replicated exactly and administered to new patients with precise directions. In the case of Perry, it’s not especially clear just what it means to replicate the program. Was the intervention simply attending a preschool? Doubtful. Was it attending a “high quality” preschool? OK, but just what made Perry “high quality”? Was it the student-teacher ratio? Home visits? The curriculum? All of the above? How exactly does one know?

Unless we know, it’s tough to be confident that policies will deliver the desired results. Worse, the bigger the policy, the bigger the risk that getting things wrong will have adverse consequences. And yet much of the support for universal preschool proceeds with a blind assurance that leaves all such difficult questions aside. For instance, last fall, the Biden administration’s Build Back Better proposal for universal preschool contained sweeping new federal requirements that would’ve governed preschool education standards, credentials, and pay. There was little evidence behind any of this and actually more cause to fear the ways in which it might bureaucratize early-childhood education, squelch small church-based and neighborhood providers, drive up costs, and aggravate staffing challenges. We would’ve been far better served not by assertions of “the science says” but by serious discussions of uncertainty, trade-offs, and sensible compromise.

Helping ensure that families have access to reliable, trusted child-care options is a good idea. But the claims made about preschool—and especially about universal pre-K—tend to be overstated, the benefits are far from certain, and fail to consider that massive new programs can cause real harm. We should keep working to expand access to reliable, convenient, trusted preschool. But we should do so guided by good judgment, not by overreliance on not-so-certain science.

This paper has addressed a classic topic: the optimal accumulation of capital and the welfare effects of government debt in neoclassical growth models. Over the past several decades, the thinking of many economists has been shaped by the Solow growth model and the Diamond overlapping-generations model, which assume certainty and competitive markets. In these models, the real interest rate reflects the marginal product of capital. As a result, the low real interest rates experienced in recent years have tempted some economists to conclude that capital accumulation now has only small social value at the margin and, therefore, that the crowding out of capital by government debt is of little concern. The innovation in this paper is to generalize these two canonical modelsto include firms with market power. In this environment, the real interest rate earned by savers is below the net marginal product of capital. For a plausible calibration of the extended Solow model, the wedge between them is about 4 percentage points. The calibration also suggests that the U.S. economy is dynamically efficient. When market power is introduced into the Diamond model, government Ponzi schemes can have different implications for welfare than they do under competition. Even if a perpetual rollover of government debt and accumulating interest is feasible, the crowding out of capital may still reduce steady-state welfare.Previous work has established a close connection between the feasibility of government Ponzi schemes and the possibility of rational speculative bubbles. This equivalence suggests that in an overlapping-generations model with market power, rational bubbles are possible whenever government Ponzi schemes are. But unlike under competition, these bubbles may reduce welfare because they could occur even in dynamically efficient economies. By diverting saving away from capital accumulation, bubbles may depress aggregate consumption. This topic could be addressed in future research. Much previous work on dynamic efficiency and Ponzi schemes has stressed the role of uncertainty about capital returns. Under uncertainty, the risk-free real interest rate is below the expected marginal product of capital, and attempted Ponzi schemes by the government are riskyThis paper abstracts from these issues by considering models with certainty. A more realistic analysis of the U.S. economy would include both uncertainty and market power. We also leave that topic for future work.

Laurence Ball and Greg Mankiw, "Market Power In Neoclassical Growth Models,"Working Paper, April 2022, http://www.restud.com/wp-content/uploads/2022/05/MS30597manuscript.pdf

Impact of firm market power"...If typical prices are marked up 20% over marginal cost, as some of the literature suggests, the real interest rate is about 4 percentage points below the social return on capital. Correcting for the effects of market power yields an estimate of the social return of more than 8%. Because this estimated social return on capital is much higher than the economy’s growth rate, it indicates that the U.S. economy is dynamically efficient...."
Relationship with "Government Ponzi Scheme""...The impact of a government Ponzi scheme on welfare depends on two effects. The first we call the aggregate effect. Because government debt crowds out capital, it depresses aggregate consumption if the net marginal product of capital exceeds the growth rate and increases aggregate consumption in the opposite case. (This is parallel to the classic Phelps, 1961, result.) The second we call the generational effect: the introduction of government debt improves the allocation of aggregate consumption between the young and the old. This effect is similar to the Pareto improvement that can result from pay-as-you-go social security when the growth rate exceeds the interest rate (Samuelson, 1958; Weil, 2008). In a competitive economy, the aggregate and generational effects of debt work in the same direction. In an economy with market power, when the economy’s growth rate is greater than the real interest rate but less than the net marginal product of capital, the two effects conflict. These findings show that, in the presence of market power, the real interest rate by itself is a poor guide for judging the welfare effect of increased government indebtedness..."

For several years after the 2007-09 recession, roughly a million people moved to the U.S. annually. That pace started to slow during the Trump administration and fell to a trickle after the Covid-19 pandemic started. The slowdown has left the U.S. with 2.4 million fewer immigrants of working age—about 1% of the working-age population—than if pre-2017 immigration trends had continued, according to Giovanni Peri, a labor economist at the University of California, Davis....The slowdown in immigration began in 2017, when the Trump administration adopted a broad range of policies to curb both illegal and legal immigrants, including refugees, foreign recruits, international students and family members of American citizens. Those already-reduced flows were then cut in half in 2020 by the onset of the pandemic and the related restrictions.

Michelle Hackman, "Add Declining Immigration to Problems Weighing on the Labor Market,"Wall Street Journal, April 5, 2022, https://www.wsj.com/articles/add-declining-immigration-to-problems-weighing-on-the-labor-market-11649174837

Add Declining Immigration to Problems Weighing on the Labor Market

DALLAS—In an economy where nearly every industry is short of labor, nursing homes represent an extreme. Their employment has fallen 16% since 2019, and 99% of homes said last fall they didn’t have enough staff.

Burnout and stress are leading causes, but so is a less-known contributor: fewer immigrants.

Emeka Nwaokolo, 36 years old, came to the U.S. from Nigeria for a business trip and met a woman from Dallas he eventually married. He ended up working as a manager for Manchester Care Homes, which runs a network of nursing homes around Dallas.

Over the years, he has referred other Nigerian immigrants from his church to Manchester. In the past few years, though, it has become much tougher for Nigerians to emigrate to the U.S. because of more-stringent visa screening at American consulates abroad. The situation has worsened with pandemic-related backups in immigration processing.

For several years after the 2007-09 recession, roughly a million people moved to the U.S. annually. That pace started to slow during the Trump administration and fell to a trickle after the Covid-19 pandemic started.

The slowdown has left the U.S. with 2.4 million fewer immigrants of working age—about 1% of the working-age population—than if pre-2017 immigration trends had continued, according to Giovanni Peri, a labor economist at the University of California, Davis. The change is being felt as the economy rebounds and many employers struggle to replace workers who were laid off or quit since early 2020, contributing to wage pressure and inflation.

Labor shortages stem from a wide variety of factors, including early retirement, Covid-19 sickness or fear of it, lack of child care and a desire to start a business. Additionally, Mr. Peri noted, fewer immigrants are a factor. Industries with above-average levels of fore

The accommodation and food sector has a high vacancy rate, a high share of foreign-born workers and relatively low wages.

About 21% of truckers are foreign born and the vacancy rate in transportation, warehousing and utilities, which includes trucking, is 6.6%. In construction, 28% of workers are foreign born and 4.8% of jobs are vacant.

In health care and social assistance, which includes nursing homes, 17.2% of workers are foreign born, and 9% of jobs are vacant.

San Antonio home builder Ed Berlanga used to be able to call up his framer or painter and ask if they could assemble an additional crew of two or three workers to take on a new project. “They would say, ‘I have three cousins in Mexico, I’d like to get them to come work with us,’” he said.

Now, no one is coming, Mr. Berlanga said, and he faces more competition for his subcontractors’ time as home demand booms. He is also paying more for almost every service. Paint jobs, which routinely cost him $6 a square foot before the pandemic, now cost $7.50.

The smaller number of immigrants appears to be one reason wages are growing briskly in industries where vacancies are high, from hospitality to long-term care. “The numbers suggest it’s a small contributing factor to what’s happening with wage growth,” though likely dwarfed by factors such as the opportunities low-wage employees have to switch to different lines of work, said Daniel Costa, director of immigration law and policy research at the Economic Policy Institute, a left-leaning think tank.

The slowdown in immigration began in 2017, when the Trump administration adopted a broad range of policies to curb both illegal and legal immigrants, including refugees, foreign recruits, international students and family members of American citizens.

Those already-reduced flows were then cut in half in 2020 by the onset of the pandemic and the related restrictions. The U.S. closed consulates around the world, stopped processing refugees and began turning away most adult migrants at the Southern border under a new public-health authority known as Title 42.

In the 12 months ended last June 30, about 247,000 people moved to the U.S., less than a quarter of the 2016 level and half that of 2019, according to U.S. census data. Those figures don’t distinguish between people who came to the U.S. legally and those who didn’t.

Further crimping the availability of foreign-born workers are pandemic-related delays in processing work permits for those already in the U.S. There were 1.6 million pending work-permit applications at the U.S. Citizenship and Immigration Services at the end of December, up from 646,000 in 2019. It takes an average of nine to 11 months to process applications, versus three to four months at the end of 2019, according to government data.

Guito Tatawho fled violence in Haiti and now lives in Indianapolis, received a work permit in 2017 as part of his asylum application and became certified as a commercial truck driver. In 2020 he bought his own truck and began working for an Illinois trucking company, where he estimates he made $7,000 a month after truck payments.

When he applied to renew his work permit in February 2021, the immigration agency didn’t get to his case before his permit—and driver’s license—expired in August.

“I’m sitting at home right now doing nothing,” Mr. Tata said in February. “My truck is just parked somewhere. I’m losing money.”

On March 18, some seven months after the expiration, he finally got his new work permit.

A spokesman for the immigration agency said it couldn’t comment on an individual case but is aware of unprecedented processing delays and is working to fix them. Last week, the Biden administration said it plans to make expired work permits valid for many immigrants who rely on them.

The administration, in a move it said was to address labor shortages, this year made 55,000 additional visas available for temporary seasonal employers such as landscapers, hotels and ski resorts, nearly doubling the total available.

A political backlash to the administration’s handling of illegal immigration at the Southern border this year has made officials reluctant to take bigger steps to increase legal immigration, according to people familiar with the administration’s thinking.

About a fifth of consulates still aren’t processing most visas, and even those that are open are buried under a backlog of 7.5 million applications, according to State Department data.

As global travel rebounds, the department aims at “reintroducing appointments for the full range of nonimmigrant visas, including for tourism and business travel,” said a spokesman for the department’s Bureau of Consular Affairs. “We will do so in a manner consistent with science and public health.”

In long-term care, the outlook is especially striking. Demand for home health aides and nursing home care is increasing as baby boomers age. Over the past decade, an average 18% of the sector’s workforce has been foreign born. That is close to the national average, but a disproportionate share of those with hands-on jobs such as orderlies are foreign born, according to nursing home operators.

Employment in nursing-care facilities fell to 1.34 million in March from 1.6 million in mid-2019. While the U.S. has some visa programs to help foreign doctors or engineers immigrate, operators of long-term-care homes can’t recruit from abroad because the jobs they are filling wouldn’t qualify for any existing visa program.

A September survey by the American Health Care Association and the National Center for Assisted Living, sister trade groups, found 99% of nursing homes and 96% of assisted-living facilities reporting staffing shortages.

Because of those shortages, 58% of nursing homes and 28% of assisted-living facilities limited how many new clients they could take on. With fewer caregivers, industry officials say, people looking for homes or health aides sometimes can’t even get their calls to agencies returned.

Adam Lampert, the chief executive of Mr. Nwaokolo’s Dallas employer, Manchester Care Homes, and a sister company employing home health aides, said that African immigrants make up roughly 80% of his workforce, many referred to him through a church.

Recently, when his team has tried to recruit through employee networks, it has come up short. There are fewer new arrivals, the people likeliest to take caregiving jobs.

“We’re not trying to discriminate against citizens when we look for employees—quite the opposite,” Mr. Lampert said. “We’re just looking for people to fill the roles that we need. And time and again, we find it is the immigrant population that tends to respond to us.”

Mr. Nwaokolo said caregiving can be demanding, such as when he needs to change clients’ adult diapers or help them go to the bathroom. It can be emotionally taxing, too, particularly working with those who have dementia.

“You have to learn how to be patient,” he said. “You have to watch your anger, because sometimes they can hit you or maybe spit in your face.”

Mr. Nwaokolo, who had been working as a local health official in Nigeria, came to the U.S. in 2015 for a health conference in Boston, where he met his future wife. That meant finding a new job. He wanted to work in healthcare but knew his experience likely wouldn’t transfer. Caregiving appealed to him, he said, because all the children in his village were taught to care for their elders.

Among Mr. Nwaokolo’s colleagues at the Dallas company, many came to the U.S. on tourist visas and ended up staying, typically by marrying an American. Others were sponsored by immediate family members who are U.S. citizens.

His cousin Anthony Chidera Nwakor, who lives in Lagos, applied for a visa to the U.S. in 2019 and was rejected. He applied again last year and is still waiting for an appointment at the U.S. consulate in Lagos. “I want to come there and make some connections, maybe work or go to school,” he said.

Mr. Nwaokolo said he has received offers to work as a traveling home nurse for as much as triple the $15 an hour he currently earns. He has decided to stay put for now because he feels some loyalty to Mr. Lampert, the chief executive of Manchester Care Homes, who paid for his nursing training and has lent him money for his family’s medical care in Nigeria.

“This place is like a family to me,” Mr. Nwaokolo said.

“…Trump's CEA showed, based on credit transactions among manufacturers, that vehicle standards to abate a ton of CO2 cost about $163 on the margin, while even Obama said the abatement was worth only $50. i.e., tightening emissions regulations fails a cost-benefit test by a wide margin. Now Biden claims that new stricter standards pass a cost benefit test. Although this will be cast as an environmental issue, the changes have nothing to do environmental economics: (1) Consumer fuel savings get (mostly) double counted because "behavioral economics."...(2) Biden says that some tightening comes for free because 5 manufacturers had already signed a pledge with California EPA to so tighten...(3) When the above are enough to tilt the scale, all costs and benefits are discounted 3%/yr. When an extra push is needed, Biden discounts environmental benefits at 2.5% per year while everything else is discounted 3%/yr…”

More on coercive paternalism

Trump's DOT and EPA spoke forcefully against paternalism as a justification for fuel standards. If people lack knowledge, give them the knowledge rather than imposing a decision on them. Here is how they said it

"the idea that regulating fuel economy and CO2 emissions can mitigate the consequences of inadequate access to information by placing decisions that depend on access to complete information in the hands of regulators rather than buyers has superficial appeal. Yet commenters do not establish that such a drastic step is necessary to overcome any inadequacy of information, or that requiring manufacturers to supply higher fuel economy will be more effective than less intrusive approaches such as expanding the range of information available to buyers." (85 FR 24608, italics added)

In contrast, Biden's DOT and EPA say nothing like this, but instead extol the purported virtues of "behavioral economics." They do not mention less intrusive approaches, let alone show why they would have fewer net benefits.

Casey Mulligan, "New Emissions Regulations are Coercive Paternalism, not Environmental Science, or even Benevolent Paternalism,"Supply And Demand (In That Order), April 5, 2022, http://caseymulligan.blogspot.com/2022/04/emissions-regulations-are-divorced-from.html

New Emissions Regulations are Coercive Paternalism, not Environmental Science, or even Benevolent Paternalism

Trump's CEA showed, based on credit transactions among manufacturers, that vehicle standards to abate a ton of CO2 cost about $163 on the margin, while even Obama said the abatement was worth only $50. i.e., tightening emissions regulations fails a cost-benefit test by a wide margin.

Now Biden claims that new stricter standards pass a cost benefit test. Although this will be cast as an environmental issue, the new conclusion is driven by assumptions unrelated to environmental economics or climate science:

(1) Consumer fuel savings get (mostly) double counted because "behavioral economics." Specifically,

"The agency’s analysis assumes that potential car and light truck buyers value only the savings in fuel costs from purchasing a higher-MPG model they expect to realize over the first 30 months they own it. Depending on the discount rate buyers are assumed to apply, this amounts to 25-30 percent of the expected savings in fuel costs over its entire lifetime." (p. 420 of DOT's final rule)

This double counting (100 - 27.5% = 72.5% of $98 billion in fuel savings) is more than quadruple the purported $16 billion net benefit shown in Table VI-11 of the final rule.

By comparison, the gross climate benefit is purportedly $27.5 billion. i.e., they would have to more than double their already inflated "social cost of carbon" to push their thumb on the scale as vigorously as they did with "behavioral economics." See below for more on paternalism.

(2) Biden says that some tightening comes for free because 5 manufacturers had already signed a pledge with California EPA to so tighten

But this ignores that California rules, when followed by just a subset of manufacturers, do not reduce the supply of federal credits, whereas changes in federal rules do even if the federal rules are not as strict as California's. The equilibrium credit price is built into the prices paid by purchasers of new cars.

(3) When the above are enough to tilt the scale, all costs and benefits are discounted 3%/yr. When an extra push is needed, Biden discounts environmental benefits at 2.5% per year while everything else is discounted 3%/yr.

"the use of the social rate of return on capital... inappropriately underestimates the impacts of climate change for the purposes of estimating the SC-GHG.... the consumption rate of interest is the theoretically appropriate discount rate in an intergenerational context." (p. 547 of the Technical Support Document. See also p. 573 of the final rule.)

Stock-market short-termism is that rare enemy that unites left and right...Neither is correct...as a compelling short book from Harvard Law School Professor Mark Roe shows...The trickier critique is that short-term thinking leads companies to invest less than they should in long-term projects and in research and development, and to prioritize dividends and buybacks instead. If widespread, the economy would suffer. Yet, the data don’t back up the idea of broad damage to the economy. True, U.S. corporate investment in factories, equipment and suchlike is much lower, as a share of gross domestic product, than at its peak shortly before the 1980 recession. But the same applies to companies in countries where shareholders have less influence, and U.S. companies actually cut investment less. The argument that R&D is lower is simply wrong. U.S. R&D spending reached a post-World War II high in 2020, as a share of GDP. Companies are spending twice as much as in 1980. If there’s an R&D problem, it lies in the public sector, where R&D has been slashed, not the private sector.

James Mackintosh, "Quarterly Capitalism Isn’t Ruining the World,"Wall Street Journal, April 6, 2022, https://www.wsj.com/articles/quarterly-capitalism-isnt-ruining-the-world-11649237479

Quarterly Capitalism Isn’t Ruining the World

Stock-market short-termism is that rare enemy that unites left and right, management and employees, and some of the world’s biggest fund managers and consultants. On the left, “quarterly capitalism” is shorthand for venal executives and greedy shareholders abusing workers and the environment in pursuit of unsustainable profits. On the right, critics fear short-term-oriented stockholders pressure management to make dumb decisions, depressing investment and the economy.

Neither is correct, as a compelling short book from Harvard Law School Professor Mark Roe shows. And that matters for public policy, lawmakers and the dominant narrative in how America’s corporations are run. There are plenty of problems, but they need different fixes.

Start with the left-wing criticism, since it is easiest to debunk. Short-term thinking is clearly not the cause of climate change, pollution or poor treatment of staff. Indeed, it is often the exact opposite: Oil companies have to plan decades ahead before building an expensive new refinery or drilling costly offshore wells. DuPont polluted the Ohio River in West Virginia despite internal warnings about it, not because of a desire for short-term profits but because it didn’t expect to be caught. It succeeded in hiding the problem for many years, a long-term outcome.

The same goes for corporate treatment of workers: Paying only the minimum wage isn’t a short-term strategy, but it was perfectly sustainable for decades for many low-skill jobs, at least until the recent economic boom. Whatever your views on how much labor unions might hurt shareholders, opposing them is definitely not a short-termist approach: Amazon successfully fought against unionization drives for almost a quarter of a century before the first vote for an Amazon union this month.

There are genuine problems, but they are due to companies being able to offload costs—pollution, global warming, societal difficulties—onto others, not short-termism. The incentives for such dumping need fixing, and taking aim at short-term thinking will, as Prof. Roe’s book title says, be “Missing the Target.”

The trickier critique is that short-term thinking leads companies to invest less than they should in long-term projects and in research and development, and to prioritize dividends and buybacks instead. If widespread, the economy would suffer.

Yet, the data don’t back up the idea of broad damage to the economy. True, U.S. corporate investment in factories, equipment and suchlike is much lower, as a share of gross domestic product, than at its peak shortly before the 1980 recession. But the same applies to companies in countries where shareholders have less influence, and U.S. companies actually cut investment less.

The argument that R&D is lower is simply wrong. U.S. R&D spending reached a post-World War II high in 2020, as a share of GDP. Companies are spending twice as much as in 1980. If there’s an R&D problem, it lies in the public sector, where R&D has been slashed, not the private sector.

Companies might turn out to have made a horrible mistake if the cost of borrowing soars and they can’t meet the interest payments, but it has been a long-term trend, not short-term pandering to shareholders. Companies overall haven’t been starved of cash they could have invested elsewhere, contrary to what politicians claim.

Yet, it’s true that executives themselves admit to the worst sort of short-termism. Prof. Roe revisits the most compelling evidence, a widely cited study by John Graham, Campbell Harvey and Shiva Rajgopal, which found almost 80% of executives willing to accept a longer-run cost to maintain short-term earnings. Dig into the study, and while still damning—quarterly numbers shouldn’t matter this much—it turns out only 2% would sacrifice a large amount of value to smooth earnings, and 23% a moderate sacrifice.

If that’s the case, the market may have fixed the problem by itself. Companies that give up profitable projects face more nimble competitors eager to step in, or might themselves start the project later. That could explain the lack of economywide evidence of problems, if the value-adding projects do eventually go ahead.

I support some small fixes that can be done at low or no cost, such as stopping quarterly earnings guidance. But politicians should focus elsewhere, rather than try to address a problem that mostly doesn’t exist—especially since so many of the supposed solutions would give more power to executives, creating other problems.

The book is a must-read for anyone interested in markets and policy, and a great overview of the state of the evidence. But it doesn’t address a linked concern that has always bothered me, that markets are fickle in their commitment to the long term, with damaging consequences.

Investors sometimes think far too long term, bidding up dot-com stocks in the late 1990s, encouraging miners to spend billions on new holes in the ground in the early 2010s, or pushing lossmaking and sometimes zero-revenue speculative growth companies to ridiculous prices early last year. Other times they think too short term, as when fearful investors demanded companies hold on to fortress balance sheets after the 2008 financial crisis in case of a repeat, rather than invest for growth.

I’m not sure there’s a fix for this capriciousness, and it may just be one of the many imperfections of capitalism that we have to live with.

“Evidence from both recent worker surveys and historical data on quits shows that the ‘Great Resignation’ is not as unusual as one might think,” wrote Bart Hobijn... latest Economic Letter from the Federal Reserve Bank of San Francisco... Hobijn argues that prior to 2000, high waves of quits had actually been quite common during rapid economic recoveries in the postwar period, based on the Manufacturing Labor Turnover Survey, which was discontinued in 1981. “The quits waves in manufacturing in 1948, 1951, 1953, 1966, 1969, and 1973 are of the same order of magnitude as the current wave,” he said. “All of these waves coincide with periods when payroll employment grew very fast, both in the manufacturing sector and the total nonfarm sector.”...The so-called quits rate, which measures voluntary job leavers as a share of total employment, remained near a record at 2.9% in February. Roughly 4.4 million Americans quit their jobs in the month, according to Labor Department data back to 2000"

Alister Bull, "‘Great Resignation’ Not That Great After All, Fed Study Says,"Bloomberg, April 4, 2022, https://www.bloomberg.com/news/articles/2022-04-04/-great-resignation-not-that-great-after-all-fed-study-says

Great Resignation’ Not That Great After All, Fed Study Says

High levels of workers quitting their jobs -- dubbed the “Great Resignation” -- may not be that rare after all, according to the latest Economic Letter from the Federal Reserve Bank of San Francisco.

“Evidence from both recent worker surveys and historical data on quits shows that the ‘Great Resignation’ is not as unusual as one might think,” wrote Bart Hobijn, a visiting fellow with the regional Fed’s research department and a professor of economics at Arizona State University.

The so-called quits rate, which measures voluntary job leavers as a share of total employment, remained near a record at 2.9% in February. Roughly 4.4 million Americans quit their jobs in the month, according to Labor Department data back to 2000.

But Hobijn argues that prior to 2000, high waves of quits had actually been quite common during rapid economic recoveries in the postwar period, based on the Manufacturing Labor Turnover Survey, which was discontinued in 1981.

“The quits waves in manufacturing in 1948, 1951, 1953, 1966, 1969, and 1973 are of the same order of magnitude as the current wave,” he said. “All of these waves coincide with periods when payroll employment grew very fast, both in the manufacturing sector and the total nonfarm sector.”

Still, while he suggests that employers will have more success filling open positions, which should ease upward pressure on wages, he doesn’t see that happening until late 2022.

We study the labor market effects of permanent 23-50% reductions in unemployment insurance benefits available in seven states. Leveraging linked firm-establishment data,we find that establishments based in reform states experience 1.5-2.4% faster employment growth relative to the same firm's establishments in other states. Using a similar multi-state firm design, starting salaries are 1.8-7.2% lower in reform states and posted salaries for the same job fall by 1.4-5.5%. These labor supply shocks yield an average labor demand elasticity of -1.0. Our results reveal a substantial decline in match quality and worker bargaining power as UI benefits become less generous.Specifically, we study the employment and earnings responses to reforms in 7 different states which sharply cut the generosity of their state UI programs in the 2010s. The largest of these state-level reforms occurred in North Carolina in 2013. This reform is well-suited for understanding the consequences of cutting UI benefits for two reasons. First, it was larger than any previous rollback implemented in the U.S. The change simultaneously reduced the maximum weekly benefit from $535 to $350 and the maximum duration from 26 to 20 weeks. The combined reductions permanently reduced the maximum value of UI benefits by 50%. Second, the cuts were implemented based on the insolvency of North Carolina’s state UI fund, rather than local labor market conditions. This allows us to compare workers in North Carolina to other states with similar labor market trends, but which had more prudent funding of their state UI programs. Six other states also enacted sizable reductions in UI generosity, but which were more modest compared to North Carolina. These “moderate reform” states (Florida, Georgia,Kansas, Michigan, Missouri, and South Carolina) cut maximum benefit durations by 6 weeks while holding fixed weekly benefit amounts, resulting in the maximum value of benefits falling permanently by 23%. Our first key finding is that following the reform, North Carolina-based establishmentsexperience 2.4% faster employment growth than do their same-firm counterparts in other states over the two years after the reform. For the six moderate reform states, employment grows 1.5% relative to same-firm counterparts in other states. These results suggest that any contractionary effect on consumer spending and aggregate demand, or increased competition for jobs are not large enough to overturn the incentive effects of finding a new job quickly. Using data from Glassdoor, our second key finding is that the earnings of new hires fall by an economically and statistically significant 7.2% in North Carolina establishments relative to the same firm’s establishments in control states. In the moderate reform states, the corresponding effect is a 1.8% decline in starting salaries. These drops combine not only the effect for new hires transitioning from unemployment and changing jobs, but also those entering the labor force. The drop in earnings is unlikely to be explained by negative worker composition effects, as new hires in the post-reform period are not negatively selected based on demographic characteristics in the CPS. Using the Glassdoor data, we estimate that lower match quality in the form of firm and occupational downgrading can account for approximately 40% of the wage effect. The remaining 60%-a 5.9% and 1.6% drop in starting salaries in North Carolina and the moderate reform states, respectively-is due to a decline in either unobserved match quality or worker bargaining power.One limitation of the Glassdoor data is that we cannot entirely rule out unobserved degradations in match quality or unobserved changes in the composition of new hires. Whether unemployment insurance cuts are desirable from a policy perspective depends on the benefits versus costs. On the positive side, these UI reforms stimulated employment growth and lowered benefit payouts. But counterbalancing this was a reduction in the wages of new hires, due to a combination of lower match quality and reduced bargaining power. This tradeoff adds a layer of complexity to debates on the optimal level of unemployment insurance benefits.

Core of paper, "...Specifically, we study the employment and earnings responses to reforms in 7 different states which sharply cut the generosity of their state UI programs in the 2010s. The largest of these state-level reforms occurred in North Carolina in 2013. This reform is well-suited for understanding the consequences of cutting UI benefits for two reasons. First, it was larger than any previous rollback implemented in the U.S. The change simultaneously reduced the maximum weekly bene t from $535 to $350 and the maximum duration from 26 to 20 weeks. The combined reductions permanently reduced the maximum value of UI benefits by 50%. Second, the cuts were implemented based on the insolvency of North Carolina's state UI fund, rather than local labor market conditions. This allows us to compare workers in North Carolina to other states with similar labor market trends, but which had more prudent funding of their state UI programs. Six other states also enacted sizable reductions in UI generosity, but which were more modest compared to North Carolina. These “moderate reform" states (Florida, Georgia, Kansas, Michigan, Missouri, and South Carolina) cut maximum bene t durations by 6 weeks while holding fixed weekly benefit amounts, resulting in the maximum value of benefits falling permanently by 23%. We analyze North Carolina's reform separately due to its more drastic nature, and combine the six moderate reform states to gain precision. In contrast to most studies, we estimate the effects of reductions in UI generosity, rather than expansions, during a period when the labor market was recovering, rather than languishing...."

Key Findings

"... Our first key finding is that following the reform, North Carolina-based establishments experience 2.4% faster employment growth than do their same firm counterparts in other states over the two years after the reform. For the six moderate reform states, employment grows 1.5% relative to same firm counterparts in other states.These results suggest that any contractionary effect on consumer spending and aggregate demand, or increased competition for jobs are not large enough to overturn the incentive effects of finding a new job quickly. Instead, our results are more consistent with workers' employment and reservation wages depending on their outside options, which decrease when UI benefits become less generous, as suggested by Mortensen and Pissarides (1994) and Mitman and Rabinovich (2015). We focus on the wages of new hires given downward wage stickiness for already employed workers (Kahn 1997; Pissarides 2009; Haefke et al. 2013)...."

"... Using data from Glassdoor, our second key finding is that the earnings of new hires fall by an economically and statistically significant 7.2% in North Carolina establishments relative to the same firm's establishments in control states. In the moderate reform states, the corresponding effect is a 1.8% decline in starting salaries.These drops combine not only the effect for new hires transitioning from unemployment and changing jobs, but also those entering the labor force.6 The drop in earnings is unlikely to be explained by negative worker composition effects, as new hires in the post-reform period are not negatively selected based on demographic characteristics in the CPS. Using the Glassdoor data, we estimate that lower match quality in the form of rm and occupational downgrading can account for approximately 40% of the wage effect. The remaining 60%{a 5.9% and 1.6% drop in starting salaries in North Carolina and the moderate reform states, respectively is due to a decline in either unobserved match quality or worker bargaining power. One limitation of the Glassdoor data is that we cannot entirely rule out unobserved degradations in match quality or unobserved changes in the composition of new hires. We overcome this limitation by estimating the reforms' effects on posted wages for the same job, within the same rm, but across treated versus non-treated establishments. We use the near-universe of posted wages in online job ads from Burning Glass Technologies, which as noted in Hazell and Taska (2020), has the key advantage that they are not contaminated by the compositional or match quality effects which could be present in the wages of new hires. We estimate that the reforms generated a 5.5% and 1.4% reduction in posted wages, effect sizes which are nearly identical to the Glassdoor estimates that account for occupational and firm fixed effects.

"... We find that the reductions in UI generosity result in an average 2.8 week decline (8% drop) in unemployment spell lengths...."

"...The unexpected UI reductions represent exogenous negative shocks to workers' outside options, which shift labor supply curves outwards. Marginal revenue product of labor declines for two reasons: increases in employment due to lower reservation wages and reduced match quality. With two points along rms' labor demand curves, we can calculate the labor demand elasticity. Combining our estimates of the changes in employment and in posted wages from the EEOC and Burning Glass analyses, respectively,we derive an average labor demand elasticity of -1.0 across all of the 7 reform states. These are in line with historical estimates of labor demand elasticities calculated based on data from British plants and coal mines, American women following World War II, and manufacturing labor in Germany. Our elasticity lies at the higher end of estimates based on changes to the minimum wage, which could be explained by UI covering low, middle, and higher wage workers...."

Evidence

"...In Figure 1, we plot the evolution of the short-term recipiency rate over time in North Carolina, starting after the official end of the Great Recession and continuing for 9 years. The blue line in panel (a) plots the rate in North Carolina at the monthly level, while the red line plots the corresponding rate for other Southern and Midwestern states which did not change their UI program over this time period. The dashed vertical line marks the date North Carolina passed their UI reform bill, while the solid vertical line denotes the date the reform became effective for newly led claims. Prior to the implementation of the reform, North Carolina (blue line) and other states (red line) exhibit a similar level and trend for short term UI receipt. Since the economy was recovering from the Great Recession, UI participation is gradually falling in the pre-period, with some seasonal patterns present as well. After the reform, the control states' recipiency rate continues its mild decline. In sharp contrast, North Carolina's rate drops shortly after the reform's implementation.20 Two years after the reform, North Carolina's recipiency rate is 10% compared to a rate of 30% in control states. Panel (b) graphs the difference between the blue and red lines, along with 95% confidence intervals. There is no evidence for differential pre-trends, with the UI recipiency rate difference between North Carolina and the control states bouncing around zero. After the reform, the gap becomes negative and widens to a 20 percentage point difference roughly two years after the reform, with this gap persisting until the end of our sample period. Estimates for the moderate reform states can be found in panel (c). Using a dynamic event study design, there is a 5.5 percentage point reduction in the short-term UI recipiency rate in treated states relative to controls in the post-reform period. This smaller effect for moderate reform states is expected given the more extreme cuts enacted by North Carolina. These analyses confirm that the UI reforms sharply curtailed the use of UI, which was the intent of the law changes. The reduction in use is partly mechanical, as individuals were eligible for 6 fewer weeks of benefits after the reform. But it could also be partly driven by the 35% reduction in weekly bene t levels in North Carolina, which could have caused individuals to exit UI earlier..."

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.