Macro Roundup

Mary Eberstadt, "Millennials, Put Away Childish Political Things,"Wall Street Journal, November 11, 2021, https://www.wsj.com/articles/millennial-genz-family-tradition-marriage-religion-woke-1619-systemic-racism-college-11636641758

Millennials, Put Away Childish Political Things

As the election results of 2021 suggest a chill in the air for the left, the time has come to talk to younger voters—millennials and Generation Z—about America’s future. The message can be distilled in a single sentence: You’ve been robbed.

You have been robbed of something treasured by millions of less-literate Americans before you: knowledge and appreciation of your own country, and its symphonic, tumultuous, sometimes riven and always illuminating history.

According to some of today’s loudest and most influential voices, the U.S. is an irredeemable cesspool of racism and bigotry. This lie has resulted in a wide patriotism gap. In 2020, according to one poll, under a quarter of Democrats surveyed said they were extremely proud to be American, as opposed to almost three-quarters of Republicans. Only a third of Americans your age say the same.

Ask yourselves why. Most of us wouldn’t trash-talk our families. Most of us wouldn’t trash-talk our neighborhoods. Think about that the next time someone trash-talks your national home and neighborhood, your country.

One such figure, impresario of the New York Times’s “1619 Project,” says she has never regarded herself as “particularly patriotic.” Another, whose sulfurous racialism permeates elite education, says that he has never felt free in the U.S., even as his every grievance is celebrated and subsidized. If they have such a low opinion of America, what makes you think they care about Americans—including you?

Many of today’s young have been robbed, too, of another source of immaterial wealth: the consolations and joys of family life. This theft is due in part to generational arithmetic. Today’s younger adults hail from smaller networks of kinship than earlier generations. You are more likely to have lacked a father at home and to have had fewer siblings, cousins, uncles and aunts than your forbears.

The family deficit is also a consequence of ideas. Generations of thinkers have disparaged the family as the enemy of utopian schemes. In their depictions, home and hearth amount to a slaughterhouse of dreams and aspirations, especially for women. To the contrary: Unprecedented rates of abortion, fatherlessness and divorce, far from liberating you, have subtracted actual and potential loved ones from your lives. No wonder surveys show that young people are the loneliest Americans.

Some of you have been robbed of another rich inheritance: organized religion. Men and women throughout the ages concluded that humanity exists within some form of a sacred order. Alongside that understanding of the divine flourished the greatest art and science, architecture and music and human creation at large that our species has ever devised. Permanent membership in “none of the above” secularism risks relinquishing your own cultural inheritance: Western civilization.

This brings us to the political choice before you. Today’s neo-Marxism and identity politics seek to co-opt your youthful energies into a lifetime of performative rancor. Is that what you want?

The left tells you that your fellow citizens are racists, fascists, sexists, bigots and haters. This relentless negativity obliterates youthful hopes. It shrivels the youthful imagination. Worst of all, it shrinks your hearts.

Young people are designed by nature to love and to be loved with energy and magnanimity. Today’s misanthropes tell you the opposite: that humanity is a toxin on the planet, unworthy of reproduction. This counsel couldn’t be more wrong—especially for you. If loneliness is the problem, putting more people in your lives with marriage and children is the self-evident solution.

The woke arrogantly claim to own you simply by virtue of your youth. That’s because they have a lot to sell you, in commerce as well as politics. Corporations fly under cool-kid flags to market harmful products. Consider the hawking of marijuana to your demographic. Or how some tech companies have succeeded in addicting millions to social media, with harrowing consequences we’re only beginning to understand. All these lurches for your wallet might make you question the left’s authority.

Conservatism isn’t a monolith. Conservatives disagree over all sorts of ideas and policies, at times profoundly. That’s the point. Diverse as it is, conservatism is one community where you will find authentic debate—even as sanity and agreement on a few big things still prevail.

In place of today’s cynical exploitation of race, conservatism says Martin Luther King Jr. In place of identity politics that flies under rival banners and denies our common home, it says only the American flag should fly over a U.S. Embassy. In place of ever more fastidiously defined factions, it says one nation indivisible. And in place of indifference to the sufferings incurred by family decline, family subversion and postmodern cultural chaos, conservatism seeks revival.

This is the room where the grown-ups are. This is where you deserve to be too—you who know from the inside your generation’s poignant realities. You are overdue for the restoration of America’s undiminished promise and for upward moral mobility.

Miguel Faria-e-Castro estimates there have likely been 3 million "COVID-19 retirements" which would explain more than have of the decline in labor force participation, "...The figure shows that the percentage of retirees in the U.S. population (the blue line) was relatively stable at around 15.5 percent until 2008 (the vertical dashed line). That year marked not only the beginning of the Great Financial Crisis but also when the oldest Baby Boomers, those born in 1946, turned 62 years of age and became eligible to receive Social Security retirement benefits. As Baby Boomers began retiring, the percentage of retirees in the U.S. population grew to 18.3 percent in February 2020, the eve of the COVID-19 outbreak. The percentage then increased at a much faster rate, reaching 19.3 percent in August 2021. One simple way to disentangle “normal” retirements from excess retirements due to COVID-19 is to compare the predicted percentage of Baby Boomer retirements from 2008 to February 2020 (the red dashed line in the figure) with the actual percentage of all retirements. The 0.92 percent difference between the two can be interpreted as the excess retirements. Based on that number, as of August 2021, there were slightly over 3 million excess retirements due to COVID-19, which is more than half of the 5.25 million people who left the labor force from the beginning of the pandemic to the second quarter of 2021...."

Miguel Faria-e-Castro, "The COVID Retirement Boom," Federal Reserve Bank Of St. Louis, October 15, 2021, https://files.stlouisfed.org/files/htdocs/publications/economic-synopses/2021/10/15/the-covid-retirement-boom.pdf

Pew on a violent year, "... The year-over-year increase in the U.S. murder rate in 2020 was the largest since at least 1905 - and possibly ever, according to provisional data from the CDC. (Final data is not expected to differ much from the provisional data.) There were 7.8 homicides for every 100,000 people in the United States in 2020, up from six homicides per 100,000 people the year before. The rise in the nation’s murder rate last year far exceeded the 20% increase measured in 2001, which was driven by the terrorist attacks of Sept. 11....The percentage of murders that were solved - known as the “clearance rate” - declined from 61% in 2019 to 54% in 2020, according to the FBI. The murder clearance rate refers to the share of homicides that are closed through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. Clearance rates are calculated based on the number of offenses closed in a given calendar year, even if the crime may have occurred in a prior year...."

John Gramlich, "What we know about the increase in U.S. murders in 2020," Pew Research Center, October 27, 2021, https://www.pewresearch.org/fact-tank/2021/10/27/what-we-know-about-the-increase-in-u-s-murders-in-2020/

What we know about the increase in U.S. murders in 2020

The U.S. murder rate rose 30% between 2019 and 2020 - the largest single-year increase in more than a century, according to data published this month by the Centers for Disease Control and Prevention (CDC). The findings align with a separate tabulation of the nation’s murder rate published in September by the FBI.

The CDC tracks murders by analyzing information contained in death certificates. The FBI tallies murders by collecting information from thousands of law enforcement agencies across the country. Despite their different methodologies, both sources point to a sharp rise in the U.S. murder rate during the pandemic year of 2020, even as the rate itself remained well below the level of earlier decades.

Below are some key takeaways from the two new sets of government data.

1) The year-over-year increase in the U.S. murder rate in 2020 was the largest since at least 1905 - and possibly ever, according to provisional data from the CDC. (Final data is not expected to differ much from the provisional data.) There were 7.8 homicides for every 100,000 people in the United States in 2020, up from six homicides per 100,000 people the year before. The rise in the nation’s murder rate last year far exceeded the 20% increase measured in 2001, which was driven by the terrorist attacks of Sept. 11.

The CDC has not yet published the total number of murders in the U.S. in 2020. But according to the FBI, there were 21,570 murders last year, up 29% from 16,669 in 2019 and the highest annual total since 1995. The FBI figures include nonnegligent homicides as well as murders, but the agency’s figures are incomplete because not all law enforcement agencies submit data.

3) Firearms were involved in 77% of murders for which data was available in 2020, up from 73% in 2019, according to the FBI. As has long been the case, handguns were involved in a majority of gun murders, while rifles and shotguns accounted for much smaller proportions. A sizable share of gun murders in 2020 involved an unknown type of firearm.

4) The percentage of murders that were solved - known as the “clearance rate” - declined from 61% in 2019 to 54% in 2020, according to the FBI. The murder clearance rate refers to the share of homicides that are closed through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. Clearance rates are calculated based on the number of offenses closed in a given calendar year, even if the crime may have occurred in a prior year.

5) It is not yet clear why murders rose dramatically in 2020. Experts have pointed to a variety of potential causes, including the economic and societal changes brought on by the coronavirus pandemic and changes in police-community relations after the murder of George Floyd in Minnesota last year. But the exact reasons remain unclear.

The FBI data, at least, shows that murder wasn’t the only form of violent crime to go up last year. The rate of aggravated assault rose nearly 12% between 2019 and 2020. And since aggravated assault is by far the most common type of violent crime tracked by the FBI, the overall violent crime rate in the U.S. also increased in 2020, by about 5%. Two other kinds of violent crime tracked by the FBI - rape and robbery - declined in 2020.

7) Americans remain far less likely to die from murder than from other causes, including from suicide and drug overdose. The U.S. murder rate in 2020 was 42% lower than the suicide rate (13.5 deaths per 100,000 people) and 71% below the mortality rate for drug overdose (27.1 deaths per 100,000 people, as of the third quarter of 2020), the CDC data shows. As was the case with murders, drug overdoses increased sharply in 2020.

Since June 2020, Americans have also become more supportive of increasing local police funding in their communities. In a separate Center survey conducted in September, 47% of U.S. adults said they favored an increase in funding for police in their area, up from 31% last June. Support for reducing local police funding declined from 25% to 15%.

"…In this paper, we study declines in women’s labor force participation by race and ethnicity as well as the presence of children. We find that increases in labor force exits were larger for Black women, Latinas, and women living with children. In particular,we find larger increases in pandemic-era labor force exits among women living with children under age 6 and among lower-earning women living with school-age children after controlling for detailed job and demographic characteristics. Latinas and Black women also had larger increases in labor force exits during the pandemic relative to White women. Differences in the presence of children and household structure explain one-quarter of the excess labor force exits among women of color.... Table 3, which displays coefficients from our models on indicators for identifying as a certain race or ethnicity, shows that covariates can explain about two-thirds of increased exits for Latinas and a little less than one-half of the excess exits for Black women. Columns 1 and 2 show specifications where all exits are the dependent variable. In these specifications, Latinas had a 6.0 percentage point higher rate of exits compared with White women without including controls and a 2.4 percentage point higher exit rate after including controls shown in column 2 of table 2. Black women had a 4.0 percentage point higher exit rate without controls and a 2.9 percentage point higher exit rate with controls. Similar disparities and reductions upon including controls apply for exits attributed to caregiving in columns 3 and 4. When examining differences in exits combined with an interest in working, we see that Latinas are 2.5 percentage points more likely than White women to have exited the labor force, but are interested in work and Black women are 2.4 percentage points (column 5). With controls, these differences fall to statistically insignificant 1.5 percentage points and 1.8 percentage points, respectively. Increases in labor force exits were also larger among Latinas and Black women during the pandemic relative to White women..."

Katherine Lim and Mike Zabek, "Women's Labor Force Exits during COVID-19: Differences by Motherhood, Race, and Ethnicity," Federal Reserve, October 2021, https://www.federalreserve.gov/econres/feds/womens-labor-force-exits-during-covid-19-differences-by-motherhood-race-and-ethnicity.htm

"... As we show in figure 2, women in households with young and schoolage children saw larger and more sustained participation declines than women without children under age 13 in their household. In June of 2021, labor force participation rates among women in households with children aged 6 to 12 were 3 percentage points lower than in January 2020, while women in households with children aged 0 to 5 saw a 2 percentage point decline. The plot also demonstrates the wide temporal variation in participation that existed even before the pandemic, particularly for women with young children...."

"... As we show in figure 1, these employment losses translated into higher percentagepoint declines in labor force participation among prime-working-age Black women and Latinas relative to White women. The initial decline in labor force participationamong these groups was more than 4 percentage points, while White women experienced a decline of around 2 percentage points, and Asian women saw no decline in labor force participation. In the fall and winter of 2020, Black women saw an increase in their labor force participation, while participation among Latinas remained persistently lower. While men of color also experienced large declines in both employment and labor force participation early in the pandemic, labor force participation changes among men of different races and ethnicity converged beginning in the fall of 2020...."

Summary statistics, "...Table 1 shows averages and standard deviations for our outcome variables and covariates collected in the CPS. To show how differences in covariates could contribute to varying levels of exits during the pandemic, table 1 presents statistics for all women (overall) and then breaks out statistics for Latinas, Black women, and White women who were employed before the pandemic. The first two rows of table 1 show that 8 percent of women left the labor force in 2020 after being previously employed and that 5 percent did so and named caregiving as a reason. Women attributed more than one-half of labor force exits to caregiving, highlighting the importance of childcare and schooling disruptions during the pandemic. Latinas were especially likely to name caregiving, both in terms of having the highest overall proportion of exits attributed to childcare and these exits making up a high proportion of all exits. Around 3 percent of women who were previously employed exited the labor force but expressed interest in working. Like exits overall, the percentages were higher for Latinas and Black women relative to White women. These figures represent increases when compared with previous years. During the pandemic, there was a 1.7 percentage point rise in the overall share of women previously employed who, while out of the labor force, responded that they wanted a job or planned to look for work in the next year-from 1.3 to 3.0 percent of all prime-working-age women.10 Consistent with the patterns of labor force exit, the increases were larger for Black women and Latinas who saw a 3.1 percentage point and 3.0 percentage point increase in the share out of the labor force but indicated interest in working, respectively. Women living with children under age 13 had around a 3.2 percentage point increase in the share who exited the labor force but expressed interest in working compared with a 1 percentage point increase among women with no children under age 13 in the household.11 Latinas, in particular, also had covariates that predicted much more severe labor market impacts of COVID-19. Table 1 shows that Latinas were employed in occupations and industries that had larger employment declines, lower prevalences of remote working, and higher shares of workers who lost work as a result of the pandemic. Around 15 percent of Latinas had less than a high school degree, compared to 5 percent of Black women and 2 percent of White women. Additionally, Latinas earned an average weekly wage of $780 compared with $1,040 for White women. Latinas are also more likely to be living with children compared with other groups of women. Black women also had covariates predicting more severe impacts, though to a smaller degree than for Latinas. Table 1 shows that the average predicted occupational job loss for a Black woman was 23 percent, compared with 24 percent for a Latina and 19 percent for a White woman. Black women were also less likely than White women to be in occupations and industries where working from home as a result of COVID-19 was common. Black women also earn substantially less and have lower average levels of education compared with White women, and they are less likely to be married..."

Germany once again sick man of Europe?"... German industrial output in August was about 9% below its 2015 level, compared with a 2% increase for the eurozone as a whole, according to the European Union’s statistics agency. In Italy, whose manufacturers are closely tied with Germany’s, industrial output rose about 5% over the six-year period. The International Monetary Fund recently lowered its forecast for German economic growth in 2021 to 3.1%, from 3.6%. It expects Germany’s economy to recover roughly in line with France and the U.K. through 2022, then fall behind starting in 2023. The malaise is fueling a debate among business and political leaders over whether the German economy needs a reboot and what it should look like. The three parties negotiating a new coalition government following September’s election want to increase public investments, raise wages and streamline planning procedures, which could boost domestic sources of growth and make companies less dependent on foreign demand....The weakness in Germany’s economy predates the Covid-19 pandemic. German industrial output and exports began stagnating in 2017, posing a problem for an economy where some 30% of jobs and output are tied to overseas demand, roughly four times the share in the U.S. The last time growth in Germany lagged markedly behind that of European neighbors was in the late 1990s and early 2000s, before a series of unpopular economic overhauls revived the country’s competitiveness. For a few years, Germany was the world’s biggest exporter of goods....German auto production has fallen by more than 50% since 2017, to around 200,000 a month. In the nine months through September, it declined slightly from the year-earlier period, compared with a roughly 10% year-over-year increase in global light-vehicle production over the same period. Germany’s share of global motor-vehicle production fell from about 7% to 5% in the five years through 2020, the data show. Germany’s automotive industry, by far the biggest in Europe, supports about 800,000 German jobs and accounts for 5% of the nation’s overall economic output. Three-quarters of cars made in Germany are exported...."

Tom Fairless, "Germany’s Economy, Once Europe’s Engine, Is Holding It Back,"Wall Street Journal, November 8, 2021, https://www.wsj.com/articles/germany-economy-lagging-behind-europe-supply-chain-11636383954

Germany’s Economy, Once Europe’s Engine, Is Holding It Back

BAD MARIENBERG, Germany—Germany’s export-oriented economy used to be a reliable engine for pulling Europe out of slumps. Now, as the continent emerges from a pandemic torpor, Germany is lagging behind.

German manufacturers are struggling to produce cars and factory equipment because of parts and labor shortages. They face surging energy prices that are making sky-high electricity bills even higher. And they must invest hundreds of billions of dollars over coming years to meet new clean-energy standards.

The era of easy foreign trade and rapid globalization has given way to geopolitical tensions, transport bottlenecks and pressure to manufacture locally. Chinese businesses, Germany’s biggest customers, are turning into competitors. Demand for German luxury cars hangs in the balance as the world shifts toward electric vehicles.

German industrial output in August was about 9% below its 2015 level, compared with a 2% increase for the eurozone as a whole, according to the European Union’s statistics agency. In Italy, whose manufacturers are closely tied with Germany’s, industrial output rose about 5% over the six-year period.

The International Monetary Fund recently lowered its forecast for German economic growth in 2021 to 3.1%, from 3.6%. It expects Germany’s economy to recover roughly in line with France and the U.K. through 2022, then fall behind starting in 2023.

The malaise is fueling a debate among business and political leaders over whether the German economy needs a reboot and what it should look like. The three parties negotiating a new coalition government following September’s election want to increase public investments, raise wages and streamline planning procedures, which could boost domestic sources of growth and make companies less dependent on foreign demand.

If implemented, the plans would represent the most comprehensive economic overhaul in years. Some economists think they also carry significant risks.

The weakness in Germany’s economy predates the Covid-19 pandemic. German industrial output and exports began stagnating in 2017, posing a problem for an economy where some 30% of jobs and output are tied to overseas demand, roughly four times the share in the U.S.

Hans Eichel, a former German finance minister who presided over some of those reforms in 2003, said that today “the external environment is more difficult than 20 years ago. Even China is looking more and more toward internal demand.”

At Wilo SE, a pump manufacturer in northwest Germany, sales rose by more than 50% in the eight years through 2017, to €1.4 billion, or about $1.6 billion, driven mainly by new markets such as China. Since then, its sales, most of which come from outside Germany, have been roughly flat.

To guard against trade disruptions and protectionism, Chief Executive Oliver Hermes said, the company is shifting production and executives closer to its customers. It is establishing a second headquarters in Beijing and plans a third in the U.S., and will add production sites in China and India.

The shift toward more localized production could mean “less export from Germany,” Mr. Hermes said, meaning fewer jobs in its home country. The company recently said it would close a factory in Eastern Germany, cutting or shifting 120 jobs.

Like other German auto suppliers, Mann+Hummel, a manufacturer of air-filtration systems based in southern Germany, faces a tricky transition as gas and diesel engines are phased out. Its sales declined about 9% last year as global car sales slowed during the pandemic.

“Supply-chain challenges and trade disputes have put stress on our model,” said Chief Executive Kurk Wilks. Raw material prices are rising, China’s economy isn’t growing as fast, and there are labor shortages, especially in the U.S., he said. “Beyond price increases, it’s the shortages of materials, certain commodities or shipping or transport,” he said.

The company has warned it could lose sales and market share if cleaner technologies such as electric motors displace gas and diesel engines, where its expertise lies. The company has announced plans to close several production facilities.

A decline in German car production this summer, mainly due to a persistent chip shortage, was the single biggest contributor to the overall drop in industrial output over that period.

Germany’s automotive industry, by far the biggest in Europe, supports about 800,000 German jobs and accounts for 5% of the nation’s overall economic output. Three-quarters of cars made in Germany are exported.

German manufacturers have invested in electric vehicles, but such vehicles require far fewer parts than traditional ones. By 2030, 30% to 50% of all new car registrations in the European Union will need to be for electric cars if the continent is to meet its carbon-dioxide emissions targets, according to Deutsche Bank analysts.

The economy is one of the topics in the negotiations between the center-left Social Democrats, the environmentalist Greens and the pro-market FDP to form a coalition government. On Oct. 15, the three parties disclosed preliminary plans to increase public investments, especially in climate protection, high-speed internet, education, research and infrastructure.

“It will be the biggest industrial modernization project that Germany has carried out probably for over 100 years, and it will really help our economy,” said Olaf Scholz, the Social Democrats’ leader and likely future German chancellor.

After years of belt-tightening aimed at honing competitiveness, German businesses and the country’s public infrastructure are suffering from underinvestment, economists say. Germany’s net investment rate has been around 0.5% of economic output since the turn of the century, compared with about 1% for Italy and 1.5% for the U.S., according to the World Bank. German net public investment has fallen below zero as existing assets depreciate.

Some economists contend that Germany’s small national market means domestic demand alone, even the kind driven by investment rather than just consumption, will never support engineering businesses that often export 80% of their products.

“Germany will always be…an export country,” said Gordon Riske, CEO of Kion Group AG, a Frankfurt-based manufacturer of forklift trucks. “For us in particular, the revenue is outside of Germany, and we have to invest where customers are.”

Although the winners of September’s election have presented the green transition as an economic opportunity, business groups and analysts say it will add costs and endanger jobs. Higher carbon and electricity prices and investments in cleaner production processes and research will eat into already dwindling profits, they warn, especially in a manufacturing-focused, energy-hungry economy.

The country’s green-energy transition will require investments of €5 trillion through 2045, or 5.2% of Germany’s annual economic output, on average, every year, according to a study published in October by KfW, the state-owned development bank. That is considerably more than the roughly €2 trillion spent reunifying West Germany with the formerly communist East Germany in the two decades after 1990.

“Germany’s entire business model is at stake,” said Oliver Bäte, CEO of German financial-services group Allianz SE. “If we get energy transition wrong, our economic core gets into trouble and an economic crisis becomes inevitable.”

Germany’s labor force grew by almost four million during Chancellor Angela Merkel’s 16-year tenure, as strong growth sucked in older workers and immigrants. The workforce is now projected to shrink by the same amount over the next decade. Experts say reserves of fresh workers in Germany and Eastern Europe may largely be depleted.

Markus Mann, an entrepreneur in rural western Germany whose business manufactures wood pellets for use as fuel, said he recently sent a “Wanted” poster to his 80 employees, promising a €500 reward for new staff referrals. He has raised wages for his staff by 3.5%, around double the usual annual increase. Unemployment in the region is 2.8%. “I need to offer a bounty,” he said.

The three coalition parties want to cut in half the time it takes for authorities to approve new investment projects, currently a serious hurdle for businessmen like Mr. Mann. Government bureaucracy costs German firms about €55 billion a year, roughly half the total amount invested in research and development, according to Germany’s federal statistics agency.

Tesla Inc. hasn’t yet received approval for a roughly $6 billion factory outside Berlin that is expected to create 12,000 jobs. The auto maker has been building the plant for almost two years and has delayed its opening from July. The company recently built a factory in Shanghai in less than a year.

Thirty years ago, Mr. Mann said, he borrowed money from his father to build a wind turbine near his home, about 30 miles from the nearest large town. Government approval took three months, and the official assessment covered four sheets of paper and cost about €5,000 in today’s money.

More recently, he sought permission to replace his old turbines with new ones that produced 40 times as much energy. This time, the approval process lasted seven years and cost almost €300,000, for an investment worth about €5.5 million, he said.

A spokesman for local authorities said that because the new turbines are much larger than the old ones, they could have a larger environmental effect, and hence require more testing.

ElringKlinger AG, a car-parts manufacturer in southern Germany, has started producing batteries and fuel cells, part of the industry’s shift toward cleaner technologies. The new production processes are highly automated, said Chief Executive Stefan Wolf, “which means we need far fewer employees than for building internal-combustion engines.” Total employment shrank by about 7% between 2018 and 2020, to about 9,700.

“We have very high labor costs, very high energy costs, and in the last five years, we have seen an enormous increase in bureaucracy,” said Mr. Wolf. “Germany might soon be the sick man of Europe again.”

Matthew Thomas reports some evidence from the recent New York election that suggests the GOP gains in 2020 with Hispanic and Asian voters are ongoing, "... This country is in the midst of a profound realignment along axes of culture and education that are about to make race and class seem like yesterday’s news...In August, I reported that Hispanic and Asian areas of Queens swung heavily toward Donald Trump in last year’s presidential election, powering the best performance by a Republican nominee in the World’s Borough since 2004. Precincts that are more than 75% Asian swung toward Trump by 16 points in 2020. Precincts that are more than 75% Hispanic saw an even more dramatic pro-Trump swing of 25 points. Now, an analysis of Tuesday night’s returns in combination with demographic data from the 2020 census reveals that these patterns are trickling down to local elections too. In 2017, Bill De Blasio won Queens precincts that are over 75% Asian by 34 points against Republican nominee Nicole Malliotakis. This week, Adams won them by just 20 points over Sliwa: a 14-point swing toward Republicans. De Blasio won Queens precincts that are over 75% Hispanic by a whopping 70 points over Malliotakis. Adams won them by 40 points over Sliwa: a 30-point swing toward Republicans...."

Another remarkable result from Tuesday was that Vallone lost his own bid for a Civil Court judgeship to a Republican candidate with virtually no resources or professional campaign infrastructure. The Queens County judiciary is notoriously corrupt; the nomination process is tightly controlled by the Democratic machine, and judgeships are given away as patronage to party insiders. Although the judicial district that Vallone ran in does not overlap with his base in Northeast Queens - and in fact includes some of the most conservative neighborhoods in the borough - that wouldn’t have been a problem in a less polarized era. His defeat is the first time a Democratic judicial nominee anywhere in Queens has lost to a Republican in recent history.

That’s quite the statement considering that for many years, progressive pundits, consultants, and other politicos have been arguing that the Democratic Party must deepen its dependence on voters of color in order to liberate itself from the need to appeal to white people. Very often, they say this is not only a strategic necessity in an age of deepening racial polarization, but a moral imperative that is the party’s only means of honoring its commitments to black and brown people. Yet Springer portrays growing Democratic reliance on voters of color not as a vindication of that analysis, but as a burden placed on them by unreliable whites.

Here in Queens, some on the left have expressed similar sentiments. On Tuesday, Republicans held on to their last remaining City Council seat in the borough when Joanne Ariola defeated progressive Democratic nominee Felicia Singh in a 36-point landslide. Aleda Gagarin, a left-wing organizer and former City Council candidate in Central Queens, commented: “I also don't want to see any ‘hot takes’ on D32 that don't include taking white supremacy and white rage to task. Republicans didn't just ‘keep a seat.’ They used overtly racist fearmongering as a key strategy and tactic. White rage was on full display throughout this election.”

What’s doubtful is how much of a role all this played in Singh’s defeat. Majority-white precincts did indeed swing 10 points to the right over 2017, when Democrat Mike Scala lost to Republican incumbent Eric Ulrich. But Scala didn’t exactly have his finger on the pulse of Breezy Point either; he and Singh both got less than a quarter of the white vote in their respective races. In other words, Singh’s platform and identity didn’t provoke especially strong blowback in terms of actual votes, though clearly they heightened the visceral hatred Republicans felt for her. But the results from elsewhere in Queens suggest that her slide among white voters probably wasn’t even about her at all. Deepening partisan polarization is coming for everyone trying to keep the flame of outer borough Democratic viability burning, regardless of ideology or identity.

But the left can’t afford to chalk up all of our defeats to whitelash alone. This country is in the midst of a profound realignment along axes of culture and education that are about to make race and class seem like yesterday’s news. If we want to prevent this outcome, the first step is admitting that it’s happening.

This should disturb both conventional Democrats merely concerned with the party’s short-term electoral prospects as much as socialists hoping to build mass support for radical politics. At the moment, all that either faction can boast is that the other is flailing just as desperately as they are. But if the conservative turn among non-white, non-college voters continues to gain momentum, the only group that will succeed in building a party of the multiracial working class will be Republicans.

This year, Avella attempted a comeback by running for the overlapping City Council seat, currently occupied by fellow Moderate White Man Paul Vallone. But this time, his constituents decided that if they wanted a Republican to represent them, they might as well go with the real McCoy - or the real Paladino, anyway. In 2017, Vallone sailed to victory against only nominal Republican opposition and a progressive third party candidate. But this week, Democratic support collapsed among every major demographic group in the district.

Matthew Thomas, "New York Democrats Keep Losing Ground with Hispanic and Asian Voters,"Vulgar Marxism, November 4, 2021, https://vulgarmarxism.substack.com/p/new-york-democrats-keep-losing-ground

New York Democrats Keep Losing Ground with Hispanic and Asian Voters

Eric Adams cruised to victory in New York City’s mayoral election, besting Republican nominee Curtis Sliwa by nearly 40 points - a commanding performance by any standard. But Adams’ margin wasn’t uniform throughout the city. In Queens, for example, he prevailed by only 22 points - five points fewer than Bill De Blasio’s margin here in 2017. Though Adams still made an impressive showing, New York Democrats shouldn’t pop the vegan wine just yet. Troubling demographic patterns are brewing just below the top-line numbers that could indicate tougher battles are yet to come.

In August, I reported that Hispanic and Asian areas of Queens swung heavily toward Donald Trump in last year’s presidential election, powering the best performance by a Republican nominee in the World’s Borough since 2004. Precincts that are more than 75% Asian swung toward Trump by 16 points in 2020. Precincts that are more than 75% Hispanic saw an even more dramatic pro-Trump swing of 25 points. Now, an analysis of Tuesday night’s returns in combination with demographic data from the 2020 census reveals that these patterns are trickling down to local elections too.

In 2017, Bill De Blasio won Queens precincts that are over 75% Asian by 34 points against Republican nominee Nicole Malliotakis. This week, Adams won them by just 20 points over Sliwa: a 14-point swing toward Republicans. De Blasio won Queens precincts that are over 75% Hispanic by a whopping 70 points over Malliotakis. Adams won them by 40 points over Sliwa: a 30-point swing toward Republicans.

"...Mr. Youngkin carried Virginia’s mountains with 70 percent of the vote, up from Mr. Trump’s 63 percent last year, according to exit polling. And among white voters without college degrees, Mr. Youngkin won 76 percent — a stark improvement from Mr. Trump’s 62 percent in 2020 and higher than in 2016, as well....Republicans have never had a demographic stronghold as reliable as Black voters have been for Democrats, a group that delivers as many as nine out of 10 votes for the party. But some Democratic leaders are now sounding the alarm: What if rural, white voters — of which there are many — start voting that reliably Republican?....The belief was, in part, that the party had already bottomed out there, especially during the Trump era, when Republicans had run up the numbers of white voters in rural areas to dizzying new heights. Virginia, however, is proof: It can get worse. In 2008, there were only four small Virginia counties where Republicans won 70 percent or more of the vote in that year’s presidential race. Nowhere was the party above 75 percent. This year, Mr. Youngkin was above 70 percent in 45 counties — and he surpassed 80 percent in 15 of them.... “In rural America the bottom for the Democratic Party is zero,” said Ethan Winter, a senior analyst at the group Data for Progress, who studies voter behavior. “I am serious about this.”...Rural, white voters in the past in the North had historic ties to the labor movement and an affinity for the Democratic Party. Increasingly, Mr. Winter said, those voters are more akin culturally to their neighbors to the South than to their local cities and suburbs. Tom Bonier, one of the Democratic Party’s leading experts on voter data and the chief executive of TargetSmart, agreed. “You look at places in the Deep South where the white, rural vote is approaching 90 percent Republican,” he said. “That’s absolutely the concern.”...”

Astead Herndon and Shane Goldmacher, "Democrats Thought They Bottomed Out in Rural, White America. It Wasn’t the Bottom,"New York Times, November 6, 2021, https://www.nytimes.com/2021/11/06/us/rural-vote-democrats-virginia.html

Democrats Thought They Bottomed Out in Rural, White America. It Wasn’t the Bottom.

HOT SPRINGS, Va. — The increasingly liberal politics of Virginia had been a sore spot for residents of this conservative town of 499 people nestled in the Allegheny Mountains. But this past week, as Republicans stormed to marquee victories powered in part by turnout in rural areas like Bath County, local voters cheered.

“We got our Virginia back,” said Elaine Neff, a 61-year-old resident. “And we haven’t had a win in a long time.”

Ms. Neff said she cried from a mix of happiness and relief after the election. She does not want to take the coronavirus vaccine and believes Glenn Youngkin, the winning Republican candidate for governor, will relax state mandates. Outside a nearby grocery store, Charles Hamilton taunted the Democrats.

“We’re a county of old country folk who want to do what they want,” said Mr. Hamilton, 74. “They found out the hard way.”

In the jigsaw puzzle that is electoral politics, Democrats have often focused their energy on swingy suburbs and voter-rich cities, content to mostly ignore many white, rural communities that lean conservative. The belief was, in part, that the party had already bottomed out there, especially during the Trump era, when Republicans had run up the numbers of white voters in rural areas to dizzying new heights.

Virginia, however, is proof: It can get worse.

In 2008, there were only four small Virginia counties where Republicans won 70 percent or more of the vote in that year’s presidential race. Nowhere was the party above 75 percent. This year, Mr. Youngkin was above 70 percent in 45 counties — and he surpassed 80 percent in 15 of them.

“Look at some of those rural counties in Virginia as a wake-up call,” said Steve Bullock, the Democratic former governor of Montana who made a long-shot 2020 presidential run, partly on a message that his party needed to compete in more conservative parts of the country. “Folks don’t feel like we’re offering them anything, or hearing or listening to them.”

Mr. Youngkin not only won less populated areas by record margins — he was outpacing former President Donald J. Trump’s 2020 showing in even the reddest counties, including by six percentage points in Bath County — but he also successfully rolled back Democratic gains in the bedroom communities outside Washington and Richmond, where many college-educated white voters had rejected Republicanism under Mr. Trump.

The twin results raise a foreboding possibility for Democrats: that the party had simply leased the suburbs in the Trump era, while Republicans may have bought and now own even more of rural America.

Republicans have never had a demographic stronghold as reliable as Black voters have been for Democrats, a group that delivers as many as nine out of 10 votes for the party. But some Democratic leaders are now sounding the alarm: What if rural, white voters — of which there are many — start voting that reliably Republican?

“It’s not sustainable for our party to continue to tank in small-town America,” said Representative Cheri Bustos, the Illinois congresswoman who led the House Democratic campaign arm in 2020.

“We’ve got a branding problem as Democrats in way too many parts of our country,” said Ms. Bustos, who is retiring from a downstate and heavily rural Illinois seat that Mr. Trump carried twice. She called it “political malpractice” and “disrespectful to think it’s OK to run up the score in big cities and just neglect the smaller towns.”

There is no easy solution.

Many of the ideas and issues that animate the Democratic base can be off-putting in small towns or untethered to rural life. Voters in Bath County, many of whom are avid hunters and conservative evangelicals, have long opposed liberal stances on gun rights and abortions. Some Democrats urge the party to just show up more. Some believe liberal ideas can gain traction, such as universal health care and free community college. Others urge a refocus on kitchen-table economics like jobs programs and rural broadband to improve connectivity. But it is not clear how open voters are to even listening.

Representative Dean Phillips, a Democrat who flipped a Republican-held seat outside Minneapolis in 2018, said that when it comes to issues that concern rural America, his party is afflicted with a “disease of disinterest.”

He especially lamented how his party’s strategists routinely tell candidates “to fish where the Democratic fish are instead of taking that canoe out a little further out on the lake.”

“For a party that predicates itself on inclusivity,” he added, “I’m afraid we’re acting awfully exclusive.”

Mr. Phillips called for Democrats to include “geographic equity” in their agenda along with racial and economic equity, noting that he is a proud member of the state’s Democratic Party, which is formally known as the Democratic-Farmer-Labor Party. “I’m a D.F.L.-er and yet the F’s and the L’ers aren’t voting for us,” he said.

The rural share of the vote in America has been steadily shrinking, but remains sizable enough to be politically potent. National exit polling in 2020 estimated that one in five voters lived in rural or small-town America. The Democratic data firm TargetSmart, which categorized voters based on population density, labeled 30 percent of the electorate as rural.

But while some Democratic politicians now recognize the scope of their rural problem, the words of voters in Bath County expose the difficulty in finding solutions. In interviews with a dozen white, rural voters who backed Mr. Youngkin, policy was less important than grievance and their own identity politics. And the voters, fueled by a conservative media bubble that speaks in apocalyptic terms, were convinced that America had been brought to the brink by a litany of social movements that had gone too far.

A monument to Confederate soldiers stands next to the sheriff’s office in Warm Springs, a visual representation of the cultural gap between its residents and the Democratic base. The town is accessible only by a two-lane highway that winds through mountains near the West Virginia border. It’s best known for The Homestead, a luxury resort built in the late 1800s that has hosted golf tournaments, conferences for the United Nations and presidents, including William Howard Taft and Theodore Roosevelt.

Ms. Neff, who owns a hardware store adorned with images of Mr. Trump as Rambo and the Terminator, was in Washington on Jan. 6 to support the former president — but refused to go into further detail. Citing false evidence, she called the coronavirus vaccine a “poison” and said she worried that Democrats were planning extermination camps of Mr. Trump’s supporters.

Karen Williams, a Bath County resident who manages vacation rentals, said she resented the current Virginia governor, Ralph Northam, a Democrat, for keeping schools shut down during the pandemic, embracing progressive policies focused on race and removing Confederate statues and monuments. She called this an example of critical race theory, a graduate-level academic framework that has become shorthand for a contentious debate on how to teach race and racism in schools.

White children “are no longer allowed to be kids, we’re treating them like little monsters,” Ms. Williams said.

Mr. Hamilton, a veteran of the Vietnam War, said his vote for Mr. Youngkin was really a proxy vote for Mr. Trump. Of President Biden, he said, “the best thing that can happen is to get him and that woman out of there.”

John Wright, a 68-year-old retiree, said he listened only to pro-Trump programming.

“I don’t care if the media said the moon was full of cheese, and there was an astronaut who brought back some cheese,” Mr. Wright said. “If the media said it, I won’t believe it.”

Some of these voters are simply out of reach for Democrats, incompatible with the party’s embrace of Black Lives Matter, transgender rights and #MeToo.

But the politically urgent problem for Democrats is that rural America has moved faster and further from them in the last 20 years than urban America has moved away from Republicans. From 1999 to 2019, cities swung 14 percentage points toward the Democrats, according to a 2020 Pew Research Center report. At the same time, rural areas shifted by 19 percentage points toward the Republicans. The suburbs remained essentially tied.

Amanda Litman, the executive director of Run for Something, which looks for Democrats to run for local offices nationwide, said it could be challenging to recruit candidates in deep red small towns — and to lure money into what are most likely losing causes.

“We just have to try and lose by less,” she said. “And ‘investing to lose by less’ is not a fun sell to Democratic donors. But it is what it is.”

Those Democrats who do run in conservative territory often distance themselves from the national party brand. When Monica Tranel, a Democrat, kicked off her bid for Montana’s new congressional seat over the summer, she lamented how few of the people she grew up with still vote Democratic. “They feel like Democrats look down on rural America,” she said in her campaign launch video.

Ben Tribbett, a Democratic strategist in Virginia, has watched his party’s vote share in rural areas wither for three decades.

“I don’t know what our message is there,” Mr. Tribbett said. “Which is a problem, because I’m supposed to be creating content for political campaigns.”

Just how much further can the party fall?

“In rural America the bottom for the Democratic Party is zero,” said Ethan Winter, a senior analyst at the group Data for Progress, who studies voter behavior. “I am serious about this.”

Rural, white voters in the past in the North had historic ties to the labor movement and an affinity for the Democratic Party. Increasingly, Mr. Winter said, those voters are more akin culturally to their neighbors to the South than to their local cities and suburbs.

Tom Bonier, one of the Democratic Party’s leading experts on voter data and the chief executive of TargetSmart, agreed. “You look at places in the Deep South where the white, rural vote is approaching 90 percent Republican,” he said. “That’s absolutely the concern.”

Mr. Youngkin carried Virginia’s mountains with 70 percent of the vote, up from Mr. Trump’s 63 percent last year, according to exit polling. And among white voters without college degrees, Mr. Youngkin won 76 percent — a stark improvement from Mr. Trump’s 62 percent in 2020 and higher than in 2016, as well.

In Bath County, a smaller group of voters cited economic concerns for why the area has become more conservative. They spoke of a time in almost mythical terms, when both parties had a foothold in the region — before rising gas prices, inflation and stagnant wages.

Sharon Lindsay, a 69-year-old librarian, said people were offended that today’s liberals assume their area is inherently racist or bigoted. “We know they wrote us off,” Ms. Lindsay said. “They never talk to us. We never see them. And we see Republicans all the time.”

New NBER from Kerr finds innovation has become increasingly concentrated, "U.S. invention has become increasingly concentrated around major tech centers since the 1970s, with implications for how much cities across the country share in concomitant local benefits. Is invention becoming a winner-takes-all race? We explore the rising spatial concentration of patents and identify an underlying stability in their distribution. Software patents have exploded to account for about half of patents today, and these patents are highly concentrated in tech centers. Tech centers also account for a growing share of non-software patents, but the reallocation, by contrast, is entirely from the five largest population centers in 1980.Non-software patenting is stable for most cities, with anchor tenants like universities playing important roles, suggesting the growing concentration of invention may be nearing its end. Immigrant inventors and new businesses aided in the spatial transformation…Figure 1 shows annual rates of U.S. patenting for tech clusters and large population centers. Beyond these two groups, we aggregate the remaining 270 MSAs and prepare a fourth group for rural areas. The thatched portion of each series is software-related, and the solid portion is nonsoftware-related. Patents are dated by their application years, and the final period of 2015-2019 is not shown due to incomplete series with respect to patent counts given future grants will occur. The share-based metrics that we focus on for most of this paper are less sensitive to this incomplete process. The rise of the six tech centers is very stark, and Figure 2 presents these data in terms of shares. The six tech centers account for 11.3% of patents from 1975-1979, but surge to 34.2% for 2015- 2019. San Francisco’s growth is from 4.6% to 18.4%. While other groups decline in share, the magnitudes and economic importance are different. The five largest population centers show the largest drop, from 32.2% to 18.6%. By contrast, the aggregate decline for the other 270 cities, from 45.5% to 41.0%, is much less. Non-urban areas also decline from 11.0% to 6.1%....”

Evidence, "...Figure 1 shows annual rates of U.S. patenting for tech clusters and large population centers. Beyond these two groups, we aggregate the remaining 270 MSAs and prepare a fourth group for rural areas. The thatched portion of each series is software-related, and the solid portion is non-software-related. Patents are dated by their application years, and the final period of 2015-2019 is not shown due to incomplete series with respect to patent counts given future grants will occur. The share-based metrics that we focus on for most of this paper are less sensitive to this incomplete process. The rise of the six tech centers is very stark, and Figure 2 presents these data in terms of shares. The six tech centers account for 11.3% of patents from 1975-1979, but surge to 34.2% for 2015-2019. San Francisco’s growth is from 4.6% to 18.4%.While other groups decline in share, the magnitudes and economic importance are different. The five largest population centers show the largest drop, from 32.2% to 18.6%. By contrast, the aggregate decline for the other 270 cities, from 45.5% to 41.0%, is much less. Non-urban areas also decline from 11.0% to 6.1%. This reallocation is remarkable and has not been documented in prior work…”

Software vs Non-Software Patenting, “…Figures 1 and 2 suggest that software patenting is important for our understanding of spatial clustering and tech clusters. Software patents are a significant share of invention in all cities, but they account for well more than half of patents in tech clusters. Panel B in Figure 2 shows that the tech centers account for 45.4% of software patents after 2015, more than double their starting share of 20.2%. San Francisco again features prominently with 25.8% of software patents filed after 2015. This reallocation pulled from all regions. Panel B of Figure 2 shows that tech clusters are also important for non-software patents (solid lines), growing from 11.0% to 23.1% across the period. San Francisco is 11.1%. However, the share for the 270 MSAs grows slightly from 45.5% to 48.1%. The shift is instead from the five largest cities in 1980, which fall from 32.3% to 20.0%. These cities have remained mostly prosperous and often hold leading positions in important sectors (e.g., media in Los Angeles, finance in New York). But, while patents continue to increase in a super-linear relationship to city population, invention has become less coupled to the largest cities...”

Core findings, "... Our contribution is to quantify how much of the rise of tech centers like Boston, Seattle, and San Francisco since the 1970s is due to a shift of patenting from the biggest population centers in 1980 like NYC and LA. The magnitudes are large: the 13.6% reduction from the 1970s to 2015-2019 in the patent share accounted for by the five largest population centers in 1980 is comparable to the combined patenting of the 238 MSAs with the least patenting in 2015-2019….”

Tech Cluster definition, "...Defining a tech cluster requires consideration of complementary inputs to patenting like venture capital investment.7 We follow Kerr and Robert-Nicoud (2020) and Rosenthal and Strange (2020) by using two criteria that reflect the scale and density of local tech activity: 1) the city ranks among the top 15 cities for patents and venture capital investment (the scale of activity) and 2) the city holds shares for patents, venture capital, employment in R&D-intensive sectors, and employment in digital-connected occupations that exceed its population share (the density of activity).
Six metropolitan statistical areas (MSAs8) satisfy these scale and density criteria: San Francisco, Boston, Seattle, San Diego, Denver, and Austin. New York and Los Angeles are ambiguous, as the cities hold large scale but fall short on several density requirements...."
Large cities, "...In 1980, the ten most populated MSAs were New York City, Los Angeles, Chicago, Philadelphia, Detroit, San Francisco, Washington DC, Dallas-Ft. Worth, Houston, and Boston. San Francisco (#6) and Boston (#10) are two of the identified tech clusters, and the next largest is San Diego at #17 in terms of the 1980 population ranking. Our analysis focuses on the reallocation of patenting from the five largest MSAs in 1980 in terms of population that rank ahead of San Francisco to tech centers...."

Tim Lee on how he misunderstood the so called housing "bubble""...What if the big problem in the early 2000s wasn’t an excess of houses but a shortage of them?...The idea that the 2000s housing boom reflected a housing shortage probably sounds wrong to you. After all, “everyone knows” that the mid-2000s saw an unprecedented building boom. Here’s a chart of single-family housing starts between 1974 and 2006: This chart shows a clear upward trend from 1980 through 2005, with housing starts reaching an all-time record in 2005. However, single-family houses aren’t the only types of home! People also live in multi-family apartment buildings and in manufactured homes. When you factor in those additional housing types, things look different: Here I’ve stacked up the three types of housing to show the total number of homes constructed each year. Over the 30 years between 1975 and 2005, there was a consistent pattern where homebuilding peaked around 2 million units during each economic boom. Total housing production in 2005 was actually slightly below the all-time high of almost 2.3 million units reached in 1978. And even this chart makes the 2005 housing boom look more impressive than it actually was, because it doesn’t factor in population growth. A better way to judge the pace of homebuilding is to compare the growth of homes to the growth of people: Here I took the top line from the previous chart and divided it by the total number of housing units to get a growth rate of homes in red. I compare this to the growth rate of people in blue. As you can see, it used to be common for the housing stock to grow more than 2 percent in a year. But after the growth rate of homes fell below 2 percent in 1987, it never reached that level again. New homes in 2005 accounted for 1.8 percent of housing inventory, an 18-year high, but far below the peak construction rates of the late 1970s and early 1980s. And this slowdown in housing construction can’t be explained by a slowdown in population growth. The nation’s population was growing at around the same rate—just under 1 percent—in the mid-1970s and the mid-2000s...."

Tim Lee, "The 2000s housing bubble was greatly exaggerated," Full Stack Economics, November 5, 2021, https://fullstackeconomics.com/the-2000s-housing-bubble-was-greatly-exaggerated/

The 2000s housing bubble was greatly exaggerated

In 2006, the New York Times published a chart that I consider the defining image of the housing crisis. Based on data from the economist Robert Shiller, it showed how American home prices have changed over more than a century.

For a few years after 2006, that forecast seemed to come true. Housing prices fell rapidly between 2007 and 2012. But then something surprising happened.

Today you can download the same data set from Robert Shiller’s website, updated to the present day. And it shows that housing prices are now above the supposedly unsustainable levels of 2006. And that’s after adjusting for inflation.

But that leads to a question that at first glance might seem crazy: what if those same explanations largely explain the housing boom that peaked in 2006? What if the big problem in the early 2000s wasn’t an excess of houses but a shortage of them?

An alternative view of the housing crisis

That’s the thesis of Shut Out, a 2019 book by Kevin Erdmann, a scholar at the Mercatus Center. Erdmann’s book attracted little notice at the time it was published. But his thesis has gotten more plausible as housing prices have zoomed upwards over the last two years.

Erdmann argues that policymakers misdiagnosed the causes of the housing boom, and that led to catastrophic policy errors. In particular, because the Federal Reserve thought housing was overvalued in 2007, it didn’t cut rates fast enough in response to the housing crash. That helped turn what might have been only a mild, industry-specific downturn into a severe, economy-wide recession. And that recession, in turn, made the housing crisis bigger than it needed to be, since many previously solvent homeowners lost their jobs or saw their mortgages go under water.

This view is shared by Gregor Schubert, a UCLA professor who recently earned his economics PhD studying the housing crisis at Harvard.

“To me, the strongest piece of evidence in favor of the recession causing the housing crisis rather than the housing crisis causing the recession is the fact that prices snapped back up, in similar geographic configuration, after the economy has recovered,” Schubert told me.

Schubert points out that the places with the highest 2006 prices, like Los Angeles, did relatively well during the Great Recession and “took off like a rocket ship afterwards.” In contrast, cities that didn’t experience much of a bubble, like Atlanta, suffered big home price declines in the crash. That, he said, suggests that the housing crash was driven by broader macroeconomic factors more than an oversupply of homes in any particular part of the country.

If Erdmann and Schubert are right, we’re still living with the consequences of misdiagnosing the housing boom as a speculative bubble. After the crash, housing construction fell to its lowest level in decades, and remained depressed for several years. That under-production contributed to the housing shortages that now plague much of the country.

There was never a national housing glut

The idea that the 2000s housing boom reflected a housing shortage probably sounds wrong to you. After all, “everyone knows” that the mid-2000s saw an unprecedented building boom. Here’s a chart of single-family housing starts between 1974 and 2006:

And even this chart makes the 2005 housing boom look more impressive than it actually was, because it doesn’t factor in population growth. A better way to judge the pace of homebuilding is to compare the growth of homes to the growth of people:

As you can see, it used to be common for the housing stock to grow more than 2 percent in a year. But after the growth rate of homes fell below 2 percent in 1987, it never reached that level again. New homes in 2005 accounted for 1.8 percent of housing inventory, an 18-year high, but far below the peak construction rates of the late 1970s and early 1980s.

And this slowdown in housing construction can’t be explained by a slowdown in population growth. The nation’s population was growing at around the same rate—just under 1 percent—in the mid-1970s and the mid-2000s.

Metros with limited housing supply had higher prices

Of course, even if there wasn’t a national housing glut, there still might have been oversupply in particular cities. So let’s get more granular.

When people think of the housing boom, they think of two things: a boom in home prices and a boom in building construction. You can find examples of both things happening in the early 2000s. But to a large extent, they happened in different places.

Superstar cities like San Francisco, Los Angeles, New York, and Boston saw home prices soar to record levels, but these cities were building homes at rates far below the national average. Strict regulations prevented the construction of new homes, so rising demand drove big increases in home prices.

On the other hand, cities like Atlanta, Dallas, and Minneapolis had strong economies and significant housing demand in the 2000s, but they coped with it by building a lot of housing. So prices rose only modestly.

If every American city fell into one of these two categories, it might have been obvious that what was happening in the 2000s was not a “housing bubble” but simply an object lesson in the importance of adequate housing supply. Cities with ample housing supply maintained moderate home prices. Cities that didn’t, didn’t.

What made the 2000s confusing was that there was a third category of cities that had rapid housing growth and rapidly rising home prices. These included Phoenix, Las Vegas, Miami, Tampa, and California’s Inland Empire. If you wanted to make the case that the US was in the throes of an irrational housing bubble, these were the cities to focus on.

In Phoenix, for example, home prices doubled between 2001 and 2006, even though the Phoenix metro area was building homes at a record pace. New housing permits in the Phoenix area rose from 41,000 in 2001 to 63,000 in 2004—far more than the 37,000 housing permits in the much larger Los Angeles area. Yet home prices in Phoenix still rose by 17 percent between those three years—and then another 54 percent between 2004 and 2006.

The combination of rapid building and soaring prices in cities like Phoenix led many people to conclude that this was an irrational and unsustainable housing bubble. You couldn’t say the high prices were the result of limited housing supply because Phoenix was building homes at a brisk pace.

But Erdmann has a different interpretation—one that’s backed up by Schubert’s empirical work: the housing booms in these cities were largely driven by underbuilding in the superstar cities.

The spillover hypothesis

In the 1990s and early 2000s, a lot of college-educated people moved to Silicon Valley for high-paying jobs at technology companies. Housing was scarce, but the new workers made enough money to outbid locals for market-rate apartments. As rents and home prices rose, middle- and working-class locals found it harder and harder to afford housing. Every year, thousands of them fled the San Francisco Bay Area for more affordable housing elsewhere.

These lower-earning workers didn’t disperse randomly across the country. Many moved to Phoenix, which was also getting a lot of inbound migrants from Los Angeles. The overall scale of California-to-Arizona migration was massive. In 2005, for example, Arizona welcomed about 90,000 new residents from California—1.5 percent of Arizona’s population.

Many of these people weren’t wealthy enough to afford a home in Los Angeles or San Francisco, but they weren’t necessarily poor either. Many could comfortably afford to buy a house in a less expensive housing market like Phoenix.

Erdmann has lived in Phoenix since the 1990s, so he got to see this process first-hand. “You can go around and talk to people at neighborhood parties here and a good portion of them will say ‘we were in San Francisco and couldn't afford it any more,’” he told me.

A similar story applies to most of the other cities we think of as typifying the housing bubble. Around 50,000 people moved from California to Nevada in 2005, with many settling in booming Las Vegas. Other Angelenos were moving to the Inland Empire, which the Census Bureau considers a separate metropolitan area from Los Angeles but is only about an hour’s drive away.

On the East Coast, hundreds of thousands of New Yorkers and Bostonians moved to Florida in the early 2000s, driving housing booms in Orlando, Tampa, and Miami.

In effect, superstar cities were outsourcing their housing development problems. In the early 2000s, the Phoenix area was expanding its housing stock quickly to accommodate the flood of California refugees. But there’s an inherent limit to how quickly a region can build new houses. So even with the high rate of housing production in Phoenix, prices still rose.

In a empirical paper published earlier this year, Schubert found a causal connection between inter-city migration and home price spillovers. The more people who move between two cities, the more home prices will “spill over” from one city to the other. When San Francisco housing gets more expensive, people move to Phoenix and Phoenix housing gets more expensive too.

The conventional view holds that rising home prices in Phoenix and Las Vegas were driven by speculators hoping to find a “greater fool” who would pay an even higher price—or by gullible consumers, likely financed by subprime loans, who didn’t realize they were getting a bad deal. But the migration story suggests another possibility: middle-class homebuyers were coming from Los Angeles or San Francisco and simply saw Phoenix homes as a bargain.

I ran this theory by Mark Calabria, a Cato Institute scholar who ran the Federal Housing Finance Agency under President Trump.

“Migration trends very clearly show growth into Phoenix from places like California,” Calabria acknowledged. “A lot of the supply response in Phoenix was demand by people not being able to live in San Francisco. But I would still argue that we overbuilt in Phoenix at the time.”

Rents weren’t rising

Dean Baker is a left-leaning economist at the Center for Economic and Policy Research. He’s known as one of the first experts to “call” the housing bubble. Way back in 2002, he wrote an article bluntly concluding that “there is a housing bubble” that “must eventually come to an end.” Two years later, he put his money where his mouth was, selling his condo in Washington DC and renting a home while he waited for the bubble to pop. It turned out to be a smart move, as he was able to buy back into the market near its low point in 2009.

Baker has continued to defend the bubble thesis, most recently in a 2018 paper. So I thought he’d be the perfect person to poke holes in Erdmann’s arguments.

As you can see above, almost 11 percent of housing units were vacant in 2000. That was near the highest level in decades, and the vacancy rate rose even more during the early 2000s. If the nation were suffering from an acute housing shortage during this period, we should have expected vacancy rates to move in the other direction.

This is a good point as far as it goes. But I find it striking that vacancies never really decline during the 1980 to 2010 period. If vacancies had been low in the 1990s and then shot up during the 2000s, that would be strong evidence that there was an over-supply of housing. But the gradual rise makes me wonder whether there were longer-term forces pushing up the vacancy rate.

I’m not sure what that might be. Maybe people were buying more vacation homes. Maybe people were moving away from rust-belt cities and leaving vacant homes behind. Maybe the Census Bureau was getting better at counting vacant properties. Without knowing where these vacancies occurred and why, it’s hard to know if they are bubble-related or not.

Baker also points out that home prices rose much faster than rents during the early 2000s. The chart below compares inflation-adjusted rents to inflation-adjusted home prices—with both indexed to be equal in 2000.

This is a great point, and it convinces me that the 2000s housing boom wasn’t entirely driven by housing shortages in a few coastal cities. At the same time, I don’t think it comes close to proving that the run-up in prices in the early 2000s was entirely a speculative bubble.

Much of the divergence between home prices and rents during the early 2000s can be explained by falling interest rates. Suppose a family can afford to pay $1,000 per month on a mortgage. In 2000, they would have gotten a mortgage at around 8 percent; that would have enabled them to borrow around $135,000. By 2004, interest rates had fallen to 5.8 percent, so they could have borrowed $170,000 with the same $1,000 monthly payment.

So falling interest rates enabled families to buy roughly 25 percent more house in 2004 than they could buy in 2000 with the same monthly payment. That means falling interest rates can explain almost all of the 28 percent increase in the ratio of home prices and rents between 2000 and 2004.

If you had taken a snapshot of the housing market in early 2004, things would have looked pretty normal. Home prices in San Francisco and Los Angeles were soaring because of local housing shortages. In most other places, prices had risen modestly thanks to falling interest rates. There were a few places where prices seemed to be going crazy—especially Las Vegas and Miami. But this wasn’t the first time in American history that a few local housing markets had dramatic boom-and-bust cycles.

But the last two years of the housing boom are harder to explain. Between January 2004 and January 2006, national home prices rose an additional 20 percent, adjusted for inflation, at a time when rents were trending down and mortgage rates were trending up. Home prices got especially crazy in Las Vegas (up another 48 percent, inflation adjusted, in two years), Phoenix (up 64 percent), and Miami (up 52 percent).

So I think it would go too far to say there was no housing bubble at all during the 2000s. Things clearly got frothy near the end. The high prices of 2005 were probably not sustainable. Some correction was probably inevitable, at least in a few cities.

However, people wrongly concluded that the entire national housing boom of the 2000s was a speculative bubble. They came to believe that prices were far above levels that could be justified by fundamentals, and that there were millions of excess homes.

The Fed botched the housing downturn

This mistake had profound consequences because the perceived size of the housing bubble influenced decision-making by the Federal Reserve. The Fed started raising its benchmark interest rate in 2004, reaching a peak of 5.25 percent in mid-2006. Part of the Fed’s goal was to raise mortgage rates and thereby cool a housing market it viewed as overheated.

The policy worked—too well, as it turned out. New housing starts peaked in early 2006 and began to fall. By June 2007, new home starts were at their lowest level in a decade, and home prices were falling.

At this point, the Fed faced a crucial choice: when and how quickly to cut interest rates. Cutting earlier and more aggressively would have provided a cushion for the housing market by pushing down mortgage rates and luring new buyers into the market. An early rate cut also would have sent a signal that the Fed wasn’t going let the bottom fall out of the housing market. That would have boosted the confidence of homebuilders and might have limited job losses in the construction sector. This would have been a no-brainer if the Fed had believed that the housing boom had been mostly healthy with a bit of froth at the end.

But things looked different if you believed that a massive housing bubble had bequeathed the country with millions of extra housing units that needed to be “worked off.” In that scenario, aggressive rate cutting would have merely been delaying an inevitable reckoning. Even worse, it might have allowed the bubble to get even bigger, leading to an even bigger crisis a few years down the line. In this view, keeping rates high at 5.25 percent was tough but necessary medicine.

Unfortunately, the Fed took this latter approach. The central bank held rates at 5.25 percent at its June 2007 meeting and maintained the same rate in August. New housing starts plunged another 20 percent between June and September, and the decline of home prices accelerated.

Meanwhile, the employment situation was deteriorating, if subtly at first. The summer of 2007 had a couple months of negative job growth, though this was not apparent until later data revisions. Even in the good months, job growth was slower than population growth. By the time the Fed cut rates in September, it was too late to prevent the onset of the Great Recession in December 2007.

Today, many people believe that the severity of the 2007 housing crisis was made inevitable by the size of the housing bubble. But Erdmann and Schubert’s analysis convinced me that that’s not true.

A large portion of the housing boom between 2000 and 2006 was driven by the fundamentals. If there was over-building in 2004 and 2005, it was modest in scale and limited to a handful of metropolitan areas. If homes were overpriced at the end of 2005, it was probably by 10 or 15 percent nationally, not 30 percent.

If the Fed had understood this at the time and acted accordingly, it could have averted a lot of human misery. Home prices would not have fallen so much, and fewer people would have lost their jobs. That, in turn, would have limited the losses of banks that bet on the mortgage market, and might have prevented the 2008 financial crisis.

Other countries had big booms without big busts

You’re probably still skeptical of the claim that the post-bubble crash didn’t need to be so severe. So I’ll close with this chart, which compares US housing prices to some of our peer countries:

But starting in 2006, our housing market diverged from theirs. US housing prices fell by more than 30 percent. Home prices in the UK, France, and Canada zoomed upward for another year or two, then fell by 18, 8, and 7 percent, respectively. Prices then rebounded more quickly in all three countries.

It’s hard to look at this chart and maintain that the depths of the 2008 housing crash was foreordained. Canada, France, and the UK have economies similar to our own, and they all experienced housing booms like ours. But none of them suffered crashes that were anywhere close to ours. Perhaps with better policies, we could have had a smaller, gentler housing bust too.

His bottom line, “…So I think it would go too far to say there was no housing bubble at all during the 2000s. Things clearly got frothy near the end. The high prices of 2005 were probably not sustainable. Some correction was probably inevitable, at least in a few cities. However, people wrongly concluded that the entire national housing boom of the 2000s was a speculative bubble.They came to believe that prices were far above levels that could be justified by fundamentals, and that there were millions of excess homes….”

Casey Mulligan's cliff notes on work disincentives in the Build Back Better bill, 3 of the 13 he identified have been culled, "...I had been tracking 13 types. Three disappeared from today's revised bill. The Medicaid expansions appear to be less. Affordable housing was cut in half (still at $150B). Dems once aspired to allow union dues to be tax deductible but that was cut. Many other pro-union provisions remain....My summary of marginal-tax-rate and employment effects are shown in the table below...."

Casey Mulligan, "Revised Build Back Better: Cliffnotes," Supply and Demand (in that order), October 28, http://caseymulligan.blogspot.com/2021/10/revised-build-back-better-cliffnotes.html

Revised Build Back Better: Cliffnotes

I had been tracking 13 types. Three disappeared from today's revised bill. The Medicaid expansions appear to be less. Affordable housing was cut in half (still at $150B). Dems once aspired to allow union dues to be tax deductible but that was cut. Many other pro-union provisions remain.

The new childcare program has now replaced an income phaseout above 250% median income with a cliff. This change from last month's bill to lastest draft has little effect on the average marginal tax rate because the same funds are being phased out over a narrower range. I.e., a few people see very large additions to their marginal tax rate while others disincentivized with a phaseout now see no addition.

The "green energy" provisions have a lot of producer-protectionist elements in them, which add to the labor wedge much like excise taxes do. However, the green energy provisions were scaled back in today's revision.

The Obamacare expansion (pp. 1458ff) is a bit more aggressive than last month's BBB bill. The revision stops indexing -- ie health insurance gets more expensive over time and it is 100% taxpayer problem rather than plan members'.

My summary of marginal-tax-rate and employment effects are shown in the table below. More derivations are available in my report although that report refers to last-month version.

Replacing the childcare phaseout with a cliff increases the (already remarkably large) marriage disincentive because even a father with moderate earnings will, if he joins the family, push them beyond the range of eligibility. I.e., the family with married parents will likely be paying full price. The various, and extreme, provisions to inflate the full price of childcare remain in the revised bill.

Pharma scores two wins

The news came out early that this bill would not have prescription-drug price controls. But it also allows Trump's terrible rebate rule to continue. Even Biden was expecting the revised bill to contain a repeal, with much savings in corporate welfare that could be put toward "transforming America." Should I start calling it "Biden's terrible rebate rule"?

Employers are still the enemy

The revision maintains the directive for OSHA to increase employer fines by at least 10X (Section 21004). Among other things, a private-sector employer with an unvaccinated employee on the payroll will be punishable by a fine of up to $1,365,320 PLUS up to $136,532 per day that employment continues (yes, $51 million per employee per year; note that the statute dictates specific amounts and that the amounts be annually indexed). This far exceeds the social cost, if any, of employing such a person.

However, revised bill does not mandate employers to provide retirement accounts. I am expecting this onerous mandate to show up in the new retirement bills introduced this week in House and Senate, but for now this element of freedom remains in employer-employee relations.

Casey Mulligan (Most Recent) continues to track the Build Back Better sausage making, "...This table shows 14 BBB provisions with significant hidden effects on incentives to work. (My earlier post provides more detail on those incentives). The final column of the table shows an estimated impact of these hidden incentives on FTE employment, allocated among the 14 provisions. This does not include any employment effects of funding BBB with income taxes on households (Sections 138201ff) and businesses (Sections 138101ff)...."

Casey Mulligan, "3rd release of Build Back Better: 7 million less employment," Supply and Demand (in that order), November 4, 2021, http://caseymulligan.blogspot.com/2021/11/3rd-release-of-build-back-better-7.html

3rd release of Build Back Better: 7 million less employment

Last night a third release of BBB was shown to the public. Both in pages and overall economics, it is in between the 1st and 2nd editions.

First-release items resurrected yesterday

Repeal of Trump's terrible rebate rule (Section 139301). It is likely illegal so repealing it does nothing but CBO probably will credit Dems with cutting about spending by about $200B with this provision.
Drug "Price Negotiation Program" (Section 139001).
Rx "Drug Inflation Rebates" (Section 139101).
Favors to labor unions such as allowing union dues to be tax deductible (Section 138514) and giving the National Labor Relations Board new authority to levy hefty penalties on employers (Section 21006, which was also in previous releases).
E-cigarette and tobacco taxes (Section 138520).
Federal family leave (Section 130001).

Important items maintained from the 2nd release

Affordable housing (still $150B across various sections such as 40001ff).
Expanded ACA premium tax credits (Sections 137301ff).
New federal childcare (Section 23001) and preK programs (Section 23002), including massive hidden taxes on marriage.
Child Tax Credit expansion (Section 137101ff).
Partial launch of the Green New Deal (various sections such as 136001ff).
Medicaid expansions (various sections).
Privacy regulation (Section 31501).

New item: Increase annual cap for deduction of State and Local Taxes (SALT) from $10K to $72.5K (Section 137601). About 8 percent of filers are affected by this. I guess that about 6 percent of filers are workers who would go from a binding cap to a nonbinding cap. For such people, IF the federal and SALT rates remain constant, the overall marginal tax rate on labor income falls. This is my assumption for the table below, but I note that lifting the SALT cap encourages states to increase SALT rates (for everyone, not just the 8 percent) and discourages states from cutting rates. Moreover, the additional SALT revenues from those rate changes may well be spent on programs that pay people not to work.

This table shows 14 BBB provisions with significant hidden effects on incentives to work. (My earlier post provides more detail on those incentives). The final column of the table shows an estimated impact of these hidden incentives on FTE employment, allocated among the 14 provisions. This does not include any employment effects of funding BBB with income taxes on households (Sections 138201ff) and businesses (Sections 138101ff).

Casey Mulligan argues there are significant disincentives to marriage written into the current Build Back Better proposal, "...A single mother earning 75% of the median household income in her state would pay nothing for child care, regardless of how much the child’s father earned. But the father’s income counts if he is legally part of the family. A husband and wife who each earned about 75% of the median income would have to pay thousands for the same daycare. In 2022-24, the married couple would pay full price, which would likely exceed $15,000 a child a year—$30,000 for two children under 5....Meanwhile, more kids will come home from a regulated child-care facility to an unmarried parent who is out of work. More families will be willing to tolerate this kind of care, regardless of the quality of cognitive or social development, since the price is “free.” Quebec imposed “quality” regulation on its child-care market, which, a landmark study found, led to “increases in early childhood anxiety and aggression” with “little measured impact on cognitive skills.” Kids exposed to the program suffered “worse health, lower life satisfaction, and higher crime rates later in life.” The Affordable Care Act taught us the hard way that nice-sounding bill titles don’t necessarily translate to sound public policy. Anyone looking inside Build Back Better will see incentives that work against Americans who want to build stable families...."

Casey Mulligan, "The Spending Bill Is an Attack on Work and Marriage,"Wall Street Journal, October 31, 2021, https://www.wsj.com/articles/spending-build-back-better-marriage-single-mother-unemployment-welfare-childcare-11635706221

The Spending Bill Is an Attack on Work and Marriage

America’s children have suffered from ill-advised public-school closings. Now Democrats want to compound the damage with their welfare spending bill, which would push fathers out of family life and move mothers and fathers alike onto unemployment rolls.

Take Section 23001 of the latest draft of the Build Back Better bill, released on Thursday. It would create a large new federal child-care program. For each year that a couple has children under 5, being unmarried could easily save them over $10,000 annually in child-care costs compared with being married.

That’s because of how the subsidies are structured. A single mother earning 75% of the median household income in her state would pay nothing for child care, regardless of how much the child’s father earned. But the father’s income counts if he is legally part of the family. A husband and wife who each earned about 75% of the median income would have to pay thousands for the same daycare. In 2022-24, the married couple would pay full price, which would likely exceed $15,000 a child a year—$30,000 for two children under 5.

Child care is one of several provisions that would encourage even middle-income people to think seriously about single parenthood. Several Republican senators wrote to Majority Leader Chuck Schumer to object to the new marriage penalties built into Democrats’ proposed reforms to the Earned Income Tax Credit. There inevitably will be marriage penalties baked into the $150 billion the bill would spend on “affordable housing,” details to come.

Democrats will claim that their new bill at least encourages work by making child care free, but that refers only to a narrow slice of the population. Most families, especially those that don’t qualify for a full subsidy or that have older children, will pay more for child care. One reason: Under the heading of “quality regulation,” the bill requires that child-care workers be paid a “living wage” and that their earnings be “equivalent to wages for elementary educators with similar credentials and experience.”

The precise meaning of that would be left to regulators, but according to the Bureau of Labor Statistics, elementary-school teachers earned an average of $63,930 annually in 2019, compared with $25,510 for child-care workers. By that benchmark, child-care facilities would need to pay workers 151% more. Perhaps child-care workers would be required to hold master’s degrees, or be represented by unions that could otherwise limit supply as they do with kindergarten teachers.

The new child-care program and various additions to major safety-net programs such as Medicaid and “affordable housing” also discourage work. As one’s income from working increases, the amount offered by these benefit programs decreases. The marginal tax rate on working an extra hour, day or week, or improving your skills, can be extremely high.

The revised bill also allows even America’s highest-income households to receive subsidized ObamaCare insurance as long as they can’t get coverage at work. Some Americans will retire earlier or spend more time between jobs. Much of the lost wages will be replaced by more-generous ObamaCare subsidies at taxpayer expense.

I estimate that the several implicit employment and income taxes in the revised bill would increase marginal tax rates on work by about five percentage points. I expect that such a change, over five years, would reduce full-time equivalent employment by about 4%, or about five million jobs.

Meanwhile, more kids will come home from a regulated child-care facility to an unmarried parent who is out of work. More families will be willing to tolerate this kind of care, regardless of the quality of cognitive or social development, since the price is “free.”

Quebec imposed “quality” regulation on its child-care market, which, a landmark study found, led to “increases in early childhood anxiety and aggression” with “little measured impact on cognitive skills.” Kids exposed to the program suffered “worse health, lower life satisfaction, and higher crime rates later in life.”

The Affordable Care Act taught us the hard way that nice-sounding bill titles don’t necessarily translate to sound public policy. Anyone looking inside Build Back Better will see incentives that work against Americans who want to build stable families.

Richard Fry reports that over half of older American adults are now retired, unlike the recession associated with the financial crisis the pandemic has driven an uptick in retirements, "...the COVID-19 recession and gradual labor market recovery has also been accompanied by an increase in retirement among adults ages 55 and older. As of the third quarter of 2021, 50.3% of U.S. adults 55 and older said they were out of the labor force due to retirement, according to a Pew Research Center analysis of the most recent official labor force data. In the third quarter of 2019,before the onset of the pandemic, 48.1% of those adults were retired. In regard to specific age groups, in the third quarter of 2021 66.9% of 65- to 74-year-olds were retired, compared with 64.0% in the same quarter of 2019....The retirement uptick among older Americans is important because, until the pandemic arrived, adults ages 55 and older were the only working age population since 2000 to increase their labor force participation. Labor force participation for the entire working age population declined from an annual average of 67% in 2000 to 63% in 2019.This partly reflects a steep drop in participation among 16- to 24-year-olds (66% to 56%) as young people increasingly pursued schooling rather than employment. Participation has also been declining this century among the “prime working age” population, those ages 25 to 54. The overall decline in labor force participation would have been larger if adults 55 and older had not increased their labor force participation (from 32% in 2000 to 40% in 2019)...."

Richard Fry, "Amid the pandemic, a rising share of older U.S. adults are now retired," Pew Research Center, November 4, 2021, https://www.pewresearch.org/fact-tank/2021/11/04/amid-the-pandemic-a-rising-share-of-older-u-s-adults-are-now-retired/

Amid the pandemic, a rising share of older U.S. adults are now retired

As employers contend with growing numbers of younger employees quitting in the great resignation, the COVID-19 recession and gradual labor market recovery has also been accompanied by an increase in retirement among adults ages 55 and older.

The leading edge of the Baby Boomer generation reached age 62 (the age at which workers can claim Social Security) in 2008. Between 2008 and 2019, the retired population ages 55 and older grew by about 1 million retirees per year. In the past two years, the ranks of retirees 55 and older have grown by 3.5 million.

The large impact of the COVID-19 recession - February 2020 to April 2020 - on retirement differs from recent recessions and marks a significant change in a long-standing historical trend toward declining or steady retirement rates among older adults.

During the Great Recession and its aftermath, retirement rates declined. By the third quarter of 2010, 48% of adults ages 55 and older were retired, down from 50% in the same quarter of 2007. The prior recessions did not disrupt the longer-running trend of rising labor force participation and declining retirement among older Americans that began around 1997. Gradually declining retirement reflected in part rising education levels among older Americans as well as gains in their health. In addition, there were policy changes in Social Security that may have impacted retirement decisions. (For example, in 2000 the earnings test on benefit claimants was no longer applied to those who had reached full retirement age.)

The retirement uptick among older Americans is important because, until the pandemic arrived, adults ages 55 and older were the only working age population since 2000 to increase their labor force participation. Labor force participation for the entire working age population declined from an annual average of 67% in 2000 to 63% in 2019. This partly reflects a steep drop in participation among 16- to 24-year-olds (66% to 56%) as young people increasingly pursued schooling rather than employment. Participation has also been declining this century among the “prime working age” population, those ages 25 to 54. The overall decline in labor force participation would have been larger if adults 55 and older had not increased their labor force participation (from 32% in 2000 to 40% in 2019).

It is unclear whether the pandemic-induced increase in retirement among older adults will be temporary or longer lasting. Newly published labor force projections from the Bureau of Labor Statistics suggest it will be temporary. BLS projects large increases in labor force participation among older adults from 2020 to 2030, with nearly 40% of 65- to 69-year-olds being in the labor force by 2030, up from 33% in 2020.

Retirement among those 55 and older who have completed at least a bachelor’s degree rose 3 percentage points over this period. Among older adults who have a high school diploma or less the rate increased 1 point. The retirement rate increased for older adults living in metropolitan areas (2 points) and also increased for older adults in rural areas (1 point).

Using evidence from Chile Ana Cusolito, Alvaro Garcia Marin and William Maloney find Chinese competition had a net negative impact on local innovation, though local frontier firms became more competitive, "...This column analyses the impact of increased import competition from China on innovation by Chilean firms. It finds a negative overall impact of competition on innovation indicators. Low-productivity firms in particular see declines across all innovation measures, while the most productive firms experience improvements in product innovation and product quality...Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity.... We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing.Thus, Aghion et al.’s (2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support..."

Ana Cusolito, Alvaro Garcia Marin, William Maloney, "Proximity to the frontier, markups, and the response of innovation to foreign competition," Center For Economic and Policy Research, November 4, 2021, https://voxeu.org/article/proximity-frontier-markups-and-response-innovation-foreign-competition

Proximity to the frontier, markups, and the response of innovation to foreign competition

The widely accepted relationship between competition and growth sits somewhat uncomfortably with the inconclusive literature on the relationship between competition and innovation (for reviews, see Cohen 2010 or Gilbert 2006), and this is becoming increasingly relevant to the debate over trade openness and growth. Despite an extensive literature suggesting that trade liberalisation increases productivity, recent evidence from the US, Canada, and Europe generally finds negative or unclear impacts of rising import exposure on innovation (e.g. Autor et al. 2017, 2020, Bloom et al. 2016, Campbell and Mau 2021, Kueng et al. 2016).

There are conceptual reasons to think that, in practice, the effects could vary greatly with country context. Aghion et al. (2005) offer a synthesis of the view that competition is necessary to get entrepreneurs out of bed in the morning, and Schumpeter’s argument that higher rents are needed to increase innovation. They accept that firms that are far from the frontier may behave à la Schumpeter and retrench with greater competition, but they argue that firms that are closer to it may see innovation precisely as a way to escape from competition (Akcigit et al. 2018). They find supportive evidence for an inverted U-shaped relationship between industry-level innovation and competition in the UK (Hashmi and Van Biesebroeck 2016), although other evidence (e.g. Hashmi 2013, Gorodnichenko et al. 2010) has been more ambiguous. This raises the question of whether the net impact of competition on innovation might be more detrimental in emerging countries which have fewer globally competitive firms than developed economies.

To shed light on the debate, in a recent paper (Cusolito et al. 2021), we revisit this question exploiting the China shock to study the impact of increased competition on innovation in a prominent upper middle-income country, Chile, which is, perhaps, uniquely suited as a case for two important reasons. First, we are able to draw on a matched firm production-innovation panel dataset that contains information on input and product prices at the firm level and therefore allows us to generate measures of markups and efficiency (physical total factor productivity) that correspond more closely to the concepts of rents and technological leadership envisaged in the Schumpeterian literature. Second, the Chilean data permit us to study the effect of foreign competition not just on patenting, as has been the traditional focus, but on a broader range of plant performance and innovation outcomes than has been possible previously, including quality.

Chile offers a clean experiment to explore the effects mentioned above. It is the iconic ‘textbook’ well-run open economy with few micro distortions that, as elsewhere, saw levels of competition shocked by a major increase in import penetration from China. But, distinct from many trade liberalisation episodes, this shock was not accompanied by other sector-specific reforms. Hence, the effects we see are likely to be purely due to differential exposure to increased foreign competition across sectors.

Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity along the lines of Aghion et al. (2005). We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing. Thus, Aghion et al.’s (2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support.

What is striking in the Chilean case is that when leaders are defined similarly, there is no positive impact on innovation. Sequentially running our specification with different cut-offs across the physical total factor productivity distribution (TFPQ), we find positive effects only when leaders are defined as the top 10% of the distribution, which accounts for 25% of industrial value added. Intuitively, this is consistent with countries further from the frontier having few firms near the frontier. But it also means that the impact of competition on incumbent innovation will be lower than in advanced countries and, potentially, net negative, as appears to be the case in Chile.

Our findings return us to our original paradox - how to reconcile that plant productivity often appears to increase with trade liberalisation, even in developing countries, but innovation does not. We propose two possible explanations. The first one may arise from the fact that the revenue TFP (TFPR) measure commonly used in the literature to define leaders and laggards combines efficiency, quality, and rents. Thus, if greater trade exposure actually leads to higher margins, as shown by De Loecker and Goldberg (2014), then this will show up as increased ‘productivity’. Our ability to exploit product price data to work with a cleaner measure of proximity to the efficiency frontier, TFPQ, allows us to abstract from mark-ups. The second explanation may arise from the fact that it is possible that increased competition leads to one-off adjustments - shedding excess workers, for example - but does not lead to dynamic increases arising from innovation.

Clearly, a finding of limited incumbent rises in innovation and perhaps lesser confidence in previous findings of increased productivity with trade liberalisation does not dictate reducing competition. Competition works through other margins, such as the reallocation of resources from low-productivity plants to high-productivity plants and through the entry of more productive plants and the exit of less productive ones (Melitz and Redding 2021). For the same period covered in the present study, Cusolito and Maloney (2018) show that over 60% of the gains in TFPQ in Chile arose precisely from entry and exit. Liu (1993) finds that in the early phases of the Chilean reforms, much productivity growth precisely occurred along the extensive margin, which rings true given the extraordinary levels of protection and distortions being unwound at the time.

But the evidence does suggest that the positive impact of competition is likely to be greater, the larger the share of frontier-proximate firms. Hence, raising the capabilities of firms and their access to resources may be an important complement to pro-competition policies. This might include extending managerial consulting programs, strengthening local innovation systems, supporting standards compliance, fostering technology adoption, and ensuring access to longer term finance, among other firm-level initiatives.

References

Akcigit, U, S T Ates and G Impullitti (2018), “Innovation, trade policy, and globalization”, VoxEU.org, 02 July.

Aghion, P, A Bergeaud, M Lequien, M Melitz and T Zuber (2021), “Opposing firm-level responses to the China shock: horizontal competition versus vertical relationships?”, Unpublished manuscript, Harvard University.

Aghion, P, N Bloom, R Blundell, R Griffith and P Howitt (2005), “Competition and Innovation: An Inverted-U Relationship”, The Quarterly Journal of Economics 120(2): 701-728.

Aghion, P, R Blundell, R Griffith, P Howitt and S Prantl (2009), “The effects of entry on incumbent innovation and productivity”, The Review of Economics and Statistics 91(1): 20-32.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2017), “Competition from China reduced innovation in the US”, VoxEU.org, 20 March.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2020), “Foreign Competition and Domestic Innovation: Evidence from U.S. Patents”, American Economic Review: Insights 2(3): 357-374.

Bloom, N, M Draca and J Van Reenen (2016), “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity”, Review of Economic Studies 83(1): 87-117.

Campbell, D and K Mau (2021), “On ‘Trade Induced Technical Change: The Impact of Chinese Imports on Innovation, IT and Productivity’”, The Review of Economic Studies, forthcoming.

Cohen, W M (2010), “Fifty years of empirical studies of innovative activity and performance”, Handbook of the Economics of Innovation 1: 129-213.

Cusolito, A P, A Garcia-Marin and W F Maloney (2021), “Proximity to the Frontier, Markups, and the Response of Innovation to Foreign Competition: Evidence from Matched Production-Innovation Surveys in Chile”, Policy Research Working Paper Series 9757, The World Bank.

Cusolito, A P and W F Maloney (2018), Productivity revisited: Shifting paradigms in analysis and policy, Washington, DC: World Bank Publications.

De Loecker, J and P K Goldberg (2014), “Firm Performance in a Global Market”, The Annual Review of Economics 6: 201-227.

Gilbert, R (2006), “Looking for Mr. Schumpeter: Where are we in the competition-innovation debate?”, Innovation policy and the economy 6: 159-215.

Gorodnichenko, Y, J Svejnar and K Terrell (2010), “Globalization and innovation in emerging markets”, American Economic Journal: Macroeconomics 2(2): 194-226.

Hashmi, A R (2013), “Competition and Innovation: The Inverted-U Relationship Revisited”, The Review of Economics and Statistics 95(5): 1653-1668.

Hashmi, A R and J Van Biesebroeck (2016), “The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry”, The Review of Economics and Statistics 98(1): 192-208.

Kueng, L, N Li and M-J Yang (2016), “The Impact of Emerging Market Competition on Innovation and Business Strategy”, NBER Working Papers 22840.

Liu, L (1993), “Entry-Exit, Learning, and Productivity Change Evidence from Chile”, Journal of Development Economics 42(2): 217-242.

Melitz, M J and S J Redding (2021), “Trade and Innovation”, VoxEU.org, 28 July.

"...This note takes a step towards measuring differential inflation rates across income groups by examining how quality-adjusted changes in rent have differed for households across the income distribution from 1985 to 2019. Rent is an especially important component of household spending to consider because housing services account for more than 25 percent of the household spending basket in the Consumer Price Index, and the price of housing services is measured using rent. Moreover, it would not be surprising to find material differences in rent growth across the income distribution, since housing costs have been rising more in locations where high-income households tend to live. For example, Moretti (2013) finds that rent growth from 1980 to 2000 was faster in metropolitan areas where college-educated people tend to live than in those where high-school educated people tend to live. We measure the rent changes facing households in different income groups by calculating changes in rent for individual housing units in the American Housing Survey and grouping rent growth observations according to the income of the resident. We find that rent growth differentials across groups were fairly minor overall. Rent growth was slightly faster for households at the top of the income distribution from 1985 to 2001, but this differential disappeared in the second half of the sample. Cumulating over the entire period, rent increased by 81 percent at the top quintile, and by 72 percent at the bottom quintile....Figure 5 shows these expenditure shares. Lower income households spend a larger fraction of consumption on housing, and all households have spent a growing fraction of consumption on housing over time...."

We can create a general price index for each income group by assuming that the prices of all other goods and services purchased by households are the same across income groups. We use the Tornqvist price index formula below:

(1) 1+πit−1,t=exp(sih,t−1+sih,t2ln(pih,tpih,t−1)+(1−sih,t−1)+(1−sih,t)2ln(pxh,tpxh,t−1))(1) 1+πt−1,ti=exp(sh,t−1i+sh,ti2ln(ph,tiph,t−1i)+(1−sh,t−1i)+(1−sh,ti)2ln(pxh,tpxh,t−1))

Where πit−1,tπt−1,ti is the inflation rate for income group ii between time period t−1t−1 and time tt, sih,tsh,ti is the housing expenditure share out of all household expenditures for income group ii in time tt, pih,tph,ti is the price index for housing for income group ii in time tt, and pxh,tpxh,t is the price index for other goods and services in time tt. For the price of housing services, we use an index created from the median rent growth rates of each income group. We calculate housing expenditure shares for each income group using data from the Consumer Expenditure Survey. In keeping with the CPI methodology, these expenditure shares are based on rental expenditures of renter households and the owners' estimate of the rent for their home for owner-occupied households. After calculating a housing expenditure share for each household, we take averages by year and income quintile. Figure 5 shows these expenditure shares. Lower income households spend a larger fraction of consumption on housing, and all households have spent a growing fraction of consumption on housing over time.

One final issue to address is that we are using median rent growth to reflect the rent inflation experienced by each income group, whereas the CPI is designed to reflect average inflation experiences. Since median rent growth tends to be below the average (as we showed in Figure 2), simply using the medians for the price of housing services used in equation 1 would result in a general price index with lower growth than the CPI. We address this issue by calculating the difference between median rent growth in the AHS (across all income groups) and the growth rate of the CPI for tenants' rent for each two-year period. Then we add this difference to the median rent growth for each income quintile.8 Thus, the average rent inflation rate across income quintiles will be the same as in the CPI, while we preserve the differences across income groups calculated in the AHS.

Table 3 reports the general inflation estimates by household income group. In the first half of the sample, the estimates turn out to be quite similar across groups. Even though the higher income groups tended to experience higher rent growth, lower income groups tended to spend a larger fraction of their consumption on housing. Since rent growth even for the bottom quintile was faster than inflation for other goods and services, the higher consumption share for the bottom quintile boosts the general inflation rate for that group by more than the top income quintile. This effect turns out to roughly offset the higher rent growth faced by the higher income quintile during this period. In the second half of the period, the general inflation rate is a bit higher for lower income groups, owing to both their slightly faster rent growth and their higher share of housing services in consumption. Cumulating over the entire sample period, the differences across income groups are relatively small.

Concluding thoughts

Differences in the price of housing services and the fraction of expenditures devoted to housing have not led to material differences in inflation across income groups over the past three decades. This result may be fairly surprising given prior research that has found substantial differences in housing costs in areas where high income households tend to live relative to areas where low income households tend to live (Moretti 2013; Gyourko, Mayer and Sinai 2013). It turns out that these geographic differences in housing costs overstate the rent growth differences faced by households in different income groups. One plausible explanation could be that lower income households are more likely to move out of areas with rising rents, bringing down average rent growth in areas where these households are observed living relative to the rent growth that we calculate absent this migration response. Also, households could sort differentially across neighborhoods within metropolitan areas in a way that reduces differences in rent growth across groups relative to the rent differences across metropolitan areas. Relatedly, rent growth tends to differ between single-family and multifamily units, even within the same neighborhood (Adams and Verbrugge 2021), which could lead to differences across income groups since higher income households are more likely to live in single-family homes. More research to understand why differential rent growth across metropolitan areas has not led to material differences in rent growth across the income distribution would be fruitful.

This article adds to the body of research that has investigated differences in prices paid or inflation rates faced by households across the income distribution (Argente and Lee 2020, Jaravel 2019, Kaplan and Schulhofer-Wohl 2017). However, the scanner data used in prior research accounts for only about 10 percent of the household consumption basket and rent accounts for an additional 25 to 30 percent, leaving more than half of the household consumption bundle for which differential inflation rates by income group have still been unexplored. More research should be done for these other types of household consumption to improve our understanding of how much and why inflation differs by income group.

References

Adams, Brian and Randal Verbrugge. 2021. "Location, Location, Structure Type: Rent Divergence within Neighborhoods" Federal Reserve Bank of Cleveland Working Paper 2103.

Garner, Thesia J. and Randal Verbrugge. 2009. "Reconciling User Costs and Rental Equivalence: Evidence from the US Consumer Expenditure Survey." Journal of Housing Economics 18: 172-192.

Gyourko, J., Mayer, C. and Sinai, T., 2013. Superstar cities. American Economic Journal: Economic Policy, 5(4),pp.167-99.

Heston, Alan and Alice O. Nakamura. 2009. "Questions about the Equivalence of Market Rents and User Costs for Owner Occupied Housing." Journal of Housing Economics 18: 273-279.

Jaravel, Xavier. 2019. "The Unequal Gains from Product Innovations: Evidence from the US Retail Sector" Quarterly Journal of Economics 715-783.

Kaplan, Greg and Sam Schulhofer-Wohl. 2017. "Inflation at the Household Level" Journal of Monetary Economics 91: 19-38.

Moretti, Enrico. 2013. "Real Wage Inequality" American Economic Journal: Applied Economics 5(1): 65-103.

Semega, Jessica, Melissa Kollar, Emily A. Shrider and John F. Creamer. 2020. "Income and Poverty in the United States: 2019". Current Population Reports, US Census Bureau.

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* The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. Return to text

1. Specifically, rent is used to calculate changes in the price of housing services faced by both renters and owner-occupants. Return to text

2. The purchase price of a home reflects the price to invest in housing, not the price of housing services. Similarly, owner expenditures such as mortgage and property tax payments reflect the price of owning the asset, not the price of consuming housing services. Although the user cost of owning a home should equal the price of housing services in theory, in practice some components of the user cost are difficult to measure. Return to text

3. Rent is topcoded at about the 97th percentile in each year. For 1997 and later years, the topcode is equal to the average rent among topcoded observations. For earlier years, the topcode is equal to the 97th percentile. To make the early years consistent with the later years, we multiply the pre-1997 topcode value by 1.5, since the post-1997 topcode values are roughly 50 percent higher than the rent at the 97th percentile. Return to text

4. Specifically, we regress the natural logarithm of rent on year indicators, the housing characteristics, and the indicators for metropolitan area. We calculate the residual from this regression, adding back in the estimated coefficients on the year indicators. The table reports the percent change in the average residual by income group. Return to text

5. There is no indicator for renovation prior post-2013. We also drop imputed rent values. Return to text

6. These cutoffs are an annualized log change of -0.21 at the bottom and 0.29 at the top. Return to text

7. This gap diminishes if we do not trim the top and bottom 5 percent of rent changes, but without the trimming average rent growth is extremely volatile year to year. Return to text

8. This difference is about 1 percentage point, on average. Return to text

Daryl Larsen, and Raven Molloy, "Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality," Federal Reserve Board, November 5, 2021, https://www.federalreserve.gov/econres/notes/feds-notes/differences-in-rent-growth-by-income-1985-2019-and-implications-for-real-income-inequality-20211105.htm

Differences in Rent Growth by Income 1985-2019 and Implications for Real Income Inequality

Introduction

Large and growing income differentials in the US have generated a mounting interest in income inequality among economists. The average income in the highest quintile of households increased by about 70 percent in real terms from 1985 to 2019, whereas the average income of the lowest quintile only increased by 20 percent during this period (Semega et al. 2020). However, these estimates of real incomes use the same price index to deflate nominal incomes, which requires the assumption that all households faced the same changes in the prices of goods and services that they purchase. Differences in the types of goods and services consumed, the geographic location of the purchase, or the type of store or outlet where purchased could all contribute to meaningful differences in price inflation faced by households in different income groups.

This note takes a step towards measuring differential inflation rates across income groups by examining how quality-adjusted changes in rent have differed for households across the income distribution from 1985 to 2019. Rent is an especially important component of household spending to consider because housing services account for more than 25 percent of the household spending basket in the Consumer Price Index, and the price of housing services is measured using rent.1 Moreover, it would not be surprising to find material differences in rent growth across the income distribution, since housing costs have been rising more in locations where high-income households tend to live. For example, Moretti (2013) finds that rent growth from 1980 to 2000 was faster in metropolitan areas where college-educated people tend to live than in those where high-school educated people tend to live.

We measure the rent changes facing households in different income groups by calculating changes in rent for individual housing units in the American Housing Survey and grouping rent growth observations according to the income of the resident. We find that rent growth differentials across groups were fairly minor overall. Rent growth was slightly faster for households at the top of the income distribution from 1985 to 2001, but this differential disappeared in the second half of the sample. Cumulating over the entire period, rent increased by 81 percent at the top quintile, and by 72 percent at the bottom quintile.

To assess the implications for a general measure of inflation, we allow the price of housing services to vary by income quintile and assume that the prices of all other goods and services are the same across groups. We also allow the expenditure share on housing services to vary by income quintile, since lower income households tend to spend a larger fraction of their consumption on housing than higher income households. In the first half of our sample, the differences in consumption baskets roughly offset the differences in rent growth across groups, leading to essentially no difference in general price inflation by income. In the second half of the sample, we estimate slightly higher inflation for lower income groups owing to their larger share of housing services. Cumulating over the entire period, the general price index rose by 136 percent for the top income quintile and by 143 percent for the bottom quintile. These differences are fairly minor relative to the widening in nominal income inequality during this period. Thus, on the whole, we find that accounting for differences in the price of housing services faced by households at different income levels does not change the conclusion that real income inequality expanded substantially over the past three decades.

The Case for Examining Rent

Even though only about one-third of households are renters, the Consumer Price Index uses rent to estimate changes in the price of housing services for all households because the price of housing services is not directly observable for owner-occupants.2 Specifically, for owner-occupied homes changes in rent of tenant-occupied units are weighted to more closely reflect the owner-occupied stock. In practice, this re-weighting has had little effect on the long-run trends in the estimated price of housing services. The index for rent paid by tenants increased by an average annual rate of 3.2 percent from 1985 to 2020, while the index for owners' equivalent rent increased by an average annual rate of 3.1 percent over this period. Consequently, in this note we will use rent paid by tenants to proxy for the price of housing services of all households, whether renters or owner-occupants.

One concern with the focus on rent is that there are fewer renters in higher segments of the income distribution. As shown by Figure 1, only 12 percent of households in the top income quintile in 2019 were renters. Because renting is so uncommon for this group, the rents that are observed may be a poor proxy for the price of housing services of owner-occupied homes in this group. Indeed, research has found that the ratio of rent to prices tends to be lower for high-value homes, signaling that these two types of units may not be comparable for high-income households (Garner and Verbrugge 2009, Heston and Nakamura 2009). That said, because the average growth rate of owners' equivalent rent was almost the same as the average growth rate of tenants' rent over our sample period, we maintain that changes in the rents paid by high-income households should provide a reasonable proxy for the changes in the price of housing services at the top of the income distribution, at least for the purpose of calculating long-run average growth rates in a CPI-type framework.

The American Housing Survey is a nationally-representative panel that tracks housing units over time. We calculate the annual rent payments for each renter household based on the reported rent and reported frequency of rent payments.3 To establish some basic facts about rent payments across the income distribution, we first regress the natural logarithm of rent on a set of indicators for each quintile of the income distribution, omitting the bottom quintile. The regression also includes a set of year indicators to abstract from the general rise in prices. The first column of Table 1 reports the results and shows that higher income households tend to pay much higher rent than lower income households. Of course, higher income households live in larger, higher quality housing units so one should not interpret these results as reflecting differences in the price of housing services. In the second column, we add controls for housing characteristics such as unit size, number of bathrooms, and the resident's rating of unit quality. These controls reduce the rent differentials across groups, but they are still large. These results suggest that households in higher income groups pay significantly more rent than households in lower income groups, even for a similar quality of housing. However, higher income households may be located in areas with higher quality amenities, in which case the higher rents would be providing them with a greater quantity of housing services. The AHS is only large enough to identify 15 metropolitan areas in all of the survey years. As shown in column 3, adding indicators for these metro areas only reduces the rent differences across income groups a bit further. It is difficult to know the extent to which unobserved housing characteristics or locations (within metro area as well as across other metro areas that are not identified in the AHS) might be contributing to the remaining differences across groups.

Rent Growth by Income Quintile

In the American Housing Survey, each housing unit is interviewed every two years. Our strategy is to calculate 2-year changes in rent for each housing unit in the sample and then group housing units according to the income of the household living in the unit in the first period. We classify housing units according to income in the first period because our goal is to estimate rent changes faced by households. Even if a household moves out and is replaced by a household with a different income, we view the first household as having faced the rent change of that housing unit. Importantly, because the American Housing Survey follows the same housing units over time, we can compute rent changes regardless of whether the resident household remains or moves out. We drop observations for units that experienced a major alteration or repair between survey years, as changes in rent would reflect changes in the quality of the unit in addition to changes in the price of housing services.5 We also drop observations below the 5th percentile and above the 95th percentile of the rent change distribution because these units also likely underwent some sort of change in quality.6 Because the survey was re-drawn in 2015, there are no units that were observed in both 2013 and 2015 and therefore we cannot calculate rent changes for that 2-year period.

Figure 2 compares the annualized growth rates in average rent in our sample to the growth rate of the CPI for tenants' rent. The average rent growth rate in the AHS tends to be fairly similar to the CPI, although it is a little lower in most 2-year periods.7 The median growth rate in the AHS has a time series pattern that is more similar to the CPI than the average AHS growth rate, although it is even lower than the average AHS growth rate. Figure 3 compares average rent growth by metropolitan area for the 15 metropolitan areas that can be identified in both datasets. The correlation is fairly high, with metropolitan areas like New York and Seattle having experienced among the strongest rent growth in both datasets, and metro areas like Detroit and Dallas having experienced the slowest rent growth. We conclude that the rent inflation experienced by the households in the AHS is a reasonable reflection of the housing services component of the CPI, although in our analysis below we will correct for the differential growth rates in the two data sources.

Dmitry Kuvshinov and Kaspar Zimmermann, "The big bang: Stock market capitalization in the long run, " Journal Of Financial Economics, September 2021, https://www.sciencedirect.com/science/article/pii/S0304405X21003962

Core finding, "...We find that between 1870 and the 1980s, advanced economy stock market capitalization grew in line with GDP, averaging to around one-third of output. Through- out this period, long-run stock market growth was driven by equity issuances while real capital gains were on average zero. After the 1980s, a sharp structural break took place with market cap to GDP ratios across advanced economies tripling and remaining persistently high. In contrast to the historical period, this recent growth acceleration was driven by rising stock prices while issuances actually slowed. The key driver behind these stock price increases was a profit shift away from other parts of the economy towards listed corporations, with listed firm profit growth far outstripping that in GDP and capital in- come. Our findings show that the post-1980s increases in US corporate profits and market capitalization are part of an unprecedented global expansion, which goes above and beyond the well-documented increases in advanced economy capital shares..."

Not explained by discount rates, "...We also show that movements in discount rates can only explain a small share of the observed capitalization increases. Even though safe interest rates declined markedly during this period, an increase in the equity risk premium meant that the rate at which future equity cash- flows are discounted fell by less than one percentage point after the 1980s, accounting for about 10% of the increase in market cap to GDP ratios. This finding is in line with several recent papers that document a stable rate of return on capital and increases in the equity premium, and connect these trends to increases in macroeconomic risk and short- ages of safe assets..."

Likely driven by the increase in profit share, "...This leaves one other potential explanation of the post- 1980s increases in market capitalization: an increase in the listed firms’ profit share. We find that this channel is key. Between 1870 and the 1980s, dividends paid by listed firms fluctuated around the level of about 1.3% of GDP. The 1990s saw listed firm dividends and earnings double, with these increases continuing into the 20 0 0s. Our counterfactual analysis shows that this channel alone can explain 70% of the post-1980s increases in the market cap to GDP ratio, and combined with the lower discount rate, it can explain almost all of the capitalization increase. Digging into the drivers of this profit shift, we show that it was driven by increases in profit per unit of sales, rather than increases in market shares (sales to gross output). This is in line with Gutiérrez and Philippon (2020), who show that there has not been a significant increase in the domestic and global market shares of dominant firms in recent decades. We further show that these profit increases go above and beyond the recently documented declines in the labor share and increases in capital income relative to GDP ( Karabarbounis and Neiman, 2013 ). The ratio of listed firms earnings to capital income has more than doubled since 1990, and those countries that experienced the largest capitalization increases during the big bang also recorded larger increases in the earnings-to-capital-income ratio. This means that the listed firm profit boom has, at least partially, come at the expense of other types of capital income...."

"...The observed long-run trends also underscore a rise in the global importance of the US equity market at the expense of the UK and France: while all three countries enjoyed roughly equal global market shares in the early 1900s,by the 1960s the US stock market accounted for close to 70% of the total 17-country capitalization....... Fig. 1 shows the ratio of stock market capitalization to GDP across the 17 countries in our sample for years 1870 to 2016.The solid blue line is the median, and the shaded area is the interquartile range of country-level data. The first century of our data saw several pronounced cycles, with the median market cap to GDP ratio doubling be- tween the early 1890s and 1910, falling back to its 1880s levels in the aftermath of World War I, increasing again in the 1950s, and falling to near-historical lows in the aftermath of the 1970s stagflation. But all these cycles were largely mean-reverting, with median stock market capitalization always eventually returning to its long-run average level of around one-third of GDP, and the cross-country..."

Dmitry Kuvshinova and Kaspar Zimmermann find that in advanced economies state market capitalization tracked GDP, but after 1980 market cap to GDP ratios tripled. They argue this structural shift was driven by an uptick in profit share towards listed firms, "...We find that between 1870 and the 1980s, advanced economy stock market capitalization grew in line with GDP, averaging to around one-third of output. Throughout this period, long-run stock market growth was driven by equity issuances while real capital gains were on average zero. After the 1980s, a sharp structural break took place with market cap to GDP ratios across advanced economies tripling and remaining persistently high. In contrast to the historical period, this recent growth acceleration was driven by rising stock prices while issuances actually slowed. The key driver behind these stock price increases was a profit shift away from other parts of the economy towards listed corporations, with listed firm profit growth far outstripping that in GDP and capital income.Our findings show that the post-1980s increases in US corporate profits and market capitalization are part of an unprecedented global expansion, which goes above and beyond the well-documented increases in advanced economy capital shares...."

Melanie Wallskog, “Entrepreneurial Spillovers Across Coworkers,” Stanford University, November 11, 2021,
http://wallskog.su.domains/files/wallskog_jmp.pdf

Firm stats, “…Beyond firm survival, I explore other measures of firm success, including size, in terms of employment, payroll, and revenue, and revenue productivity. As shown in Table9, the patterns are generally similar to those for firm survival: individuals who work with more entrepreneurs tend to start “worse" firms, unless their entrepreneurial coworkers themselves were successful. For example, in column 1 of Panel A, I find that a one standard deviation (14.7 percentage point) increase in the share of coworkers who were recently entrepreneurs predicts 5.5% lower entry year employment. Yet, as column 2 shows, this pattern is offset if the entrepreneurial coworkers ran large firms | i.e., if their entrepreneurial firms were in the top 10% of entry year log employment, amongst firms that entered in the same year and industry: conditional on general exposure to entrepreneurial coworkers, a future entrepreneur with a one standard deviation (3.8 percentage point) higher share of coworkers who were entrepreneurs to particularly large firms has 4.3% higher entry year employment.As Panel B shows, these patterns persist with the inclusion of entrepreneurial firm industry fixed effects: individuals' future entrepreneurial success is predicted by their coworkers' success, even within the industry in which they start their firm. Table 9 shows similar patterns for the likelihood of a future entrepreneur's firm being in the top 10% of entry year employment, payroll, and revenue, relative to firms that enter in the same year and industry (where I measure entrepreneurial coworkers' successes by analogous measures). The one exception is in terms of revenue productivity, where on its own, general exposure to entrepreneurs predicts a marginally higher probability of starting a particularly productive firm (column 9), but this appears to be driven by the particularly productive coworkers (column 10). Conditional on exposure to entrepreneurial coworkers, a future entrepreneur who works with a one standard deviation (4.5 percentage point) higher share of coworkers who were entrepreneurs at particular high-productivity firms is on average 0.6 percentage points more likely to run a particularly productive firm, a 9.9% increase relative to the mean outcome.57These patterns suggest that what entrepreneurial coworkers teach individuals depends on the experiences of the coworkers, with successful entrepreneurs having a greater capacity to improve future entrepreneurs' prospects. In the remainder of this section, I consider additional outcomes that complement these standard measures and provide additional insight into the mechanisms of these spillovers….”

By Sector, “…Sector of individual's establishment Because workplaces and entrepreneurship patterns vary by industry, it is plausible that spillovers may vary dramatically across sectors.45 In fact, most sectors have similar coefficients to the aggregate coefficient, with some exceptions. Figure5shows the extensive margin spillovers by the sector of the individual's current establishment, estimated in a single regression by interacting the share of coworkers who were recently entrepreneurs with indicators for each sector, while continuing to include industry fixed effects that control for baseline differences in future entrepreneurship rates. There appears to be few spillovers for workers in the agriculture, utilities, and health sectors (which likely have high entry costs due to regulation) but substantial spillovers in the management and accommodation and food services sectors.46The fact that spillovers exist in most sectors but are strongest in the accommodation and food service sector suggests two conclusions. First, these spillovers exist across the economy | these spillovers are commonplace, and are not driven by the culture or structure of a single sector. Second, because the spillovers are largest in the relatively low-technology accommodation and food services sectors, these spillovers are unlikely to be predominantly transmitting knowledge of complex technologies or promoting innovation..”

“…Table 3 presents the point estimates from model (4) as controls are gradually added. As the table shows, individuals who work with proportionally entrepreneurial coworkers are more likely to become entrepreneurs in the future, regardless of the inclusion of controls. As more controls are added, this relationship decreases marginally but remains relatively stable. In the full speciation (column 8), the coefficient on the share of coworkers with entrepreneurial experience is 0.025: this predicts that an individual whose entire set of coworkers have entrepreneurial experience is 2.5 percentage points more likely to become an entrepreneur themself, compared to an individual who works with no entrepreneurial coworkers.27Recall that only 3.1% of the sample become entrepreneurs subsequently, such that 2.5 percentage points is very large relative to 3.1%, suggesting an 80% increase relative to the mean. However, this interpretation may be misleading, since very few individuals work with all former entrepreneurs. Instead, consider an increase in one standard deviation: the estimated model predicts that individuals who work a one standard deviation (9.5 percentage points) higher share of entrepreneurial coworkers are 0.236 percentage points more likely to become entrepreneurs in the next five years. This gap is still large: a 0.236 percentage point increase in the predicted future entrepreneurship maps into a 7.6% increase relative to the mean;28 this increase is comparable to the prior findings in the literature. To provide a simple evaluation of the size of this estimate, I conduct a back-of-the-envelope calculation to approximate how much the spillovers boost aggregate entrepreneurship. I predict the number of “additional" future entrepreneurs that start firms in the presence of spillovers by multiplying the coefficient on the share with the mean share of coworkers with entrepreneurship experience (0.03356) and the number of individuals (46.68 million). This calculation yields a predicted additional 39,000 future entrepreneurs, which amounts to a 2.75% increase. While this back-of-the-envelope calculation is inherently simple, ignoring any general equilibrium forces or dynamics of how spillovers aggregate over time, it does suggest a meaningful role for spillovers: spillovers boost aggregate entrepreneurship by 3%...”

Key finding“…I find evidence of positive extensive margin spillovers: individuals who work with one standard deviation (10 percentage points) higher share of entrepreneurial coworkers are 2.5 percentage points more likely to become entrepreneurs themselves within the next five years, an 8% increase relative to the average likelihood…”

The evidence

“…Entrepreneurial coworkers It is additionally important to note that coworkers with recent entrepreneurial experience were not necessarily the most productive entrepreneurs: indeed, many of their firms no longer exist, and they are now primarily employed at a different firm. As discussed below,this likely shapes the lessons that these coworkers can teach. Table2documents summary statistics on the firm outcomes for coworkers and individuals who were entrepreneurs within the previous five years (1999-2003).The table shows the outcomes both in terms of the average coworkers (columns 1 and 2, i.e., average outcomes for coworkers who were previous entrepreneurs) and at the individual level (columns 3 and 4, i.e., average outcomes for individuals who were previous entrepreneurs). For this paper, the past outcomes of the average set of entrepreneurial coworkers (columns 1 and 2) describe the average “treatment" that individuals face in the workforce. The average past outcomes of previous entrepreneurs in general (columns 3 and 4, many of whom started their current firm) provide benchmarks for the success of the average set of entrepreneurial coworkers. Table 2 highlights that entrepreneurial coworkers generally started relatively unsuccessful (i.e., shortersurviving, smaller, and less productive) firms, although some were relatively successful. Less than half of individuals' entrepreneurial coworkers started _rms that survive to age 5, on average, while over 60% of all recent entrepreneurs' firm ssurvive that long. Alongside these higher exit rates, many entrepreneurial coworkers have since returned to being a standard worker: while around 52% of the recent entrepreneurs in general started their current firms, less than 9% of an individual's entrepreneurial coworkers are entrepreneurs of the current firms, on average. This means that many of these entrepreneurial coworkers have left the firm they started and now work at someone else's firm. Yet, some entrepreneurial coworkers started relatively successful firms; for instance, on average 15% of entrepreneurial coworkers started firms that were in the top 10% of entry year log employment, amongst firms that started in the same year and industry as them. Taken together, these summary statistics suggest that exposure to entrepreneurial coworkers is likely quite different from, e.g., mentorship through start-up accelerator programs: the average entrepreneurial coworker did not start a hyper-productive firms.However, the fact that there is heterogeneity in these entrepreneurial coworkers' outcomes | some individuals get “lucky" and work with successful former entrepreneurs | allows me to explore the roles of both exposure to more entrepreneurs and to more successful entrepreneurs in SectionsIIIandIVbelow….”

“..Despite the fact that future entrepreneurs are different from workers in general, they work and become entrepreneurs across the economy. Figure2shows the sectoral composition of all individuals and future entrepreneurs, plotting the shares of all individuals compared to the shares of future entrepreneurs employed in each 2004 primary firm sector (based on NAICS codes), along with the share of future entrepreneurs' firms' entry year sectors. As the figure shows, future entrepreneurs work in all industries in 2004 and start firms in all industries, though they disproportionately work and start firms in construction, professional/ scientific/technical services (e.g., R&D and law and accounting services), and accommodation and food services, and less often appear in manufacturing and health, compared to the general workforce. Nearly half of future entrepreneurs start firms in the same sector as their 2004 establishment….”

Workers vs. entrepreneurs, “…Entrepreneurs are quite different from the general labor force. Yet, entrepreneurs work and start firms in all sectors of the economy, making the potential scope of entrepreneurial spillovers large. For the 2004 sample, in Table1 I present entrepreneurial, demographic, job, and establishment characteristics of all individuals in 2004 and of those who become entrepreneurs between 2005 and 2009. Relative to the general population, future entrepreneurs tend to be young, male, educated, White and Asian, born outside the U.S., higher earning, and working at smaller, younger firms.They also tend to work with more entrepreneurial coworkers, which I explore more systematically in the remainder of the paper. While entrepreneurs tend to be younger than workers, there is a distinct inverse-U relationship between age and entrepreneurship, as shown in Figure 1. Entrepreneurship rates by age peak around age 35, with the share of individuals aged 35 who are entrepreneurs being nearly double that of individuals aged 20 or 70….”

Summery statistics, useful factoids

Core findings, “…In my extensive margin analysis, I estimate regression models of whether individuals who work with more former entrepreneurs in 2004 are more likely to become entrepreneurs themselves subsequently, between 2005 and 2009. I find evidence of positive spillovers: individuals who work with one standard deviation (about 10 percentage points) higher share of coworkers who were entrepreneurs in the past five years are 8% more likely to become entrepreneurs themselves in the next five years, relative to the average likelihood. Through a back of- the-envelope calculation, this pattern suggests that spillovers generate an additional 3% of entrepreneurs in the aggregate. While the average extensive margin spillover is large (8%, relative of the mean),there is substantial heterogeneity. The spillovers tend to be amplified by exposure to relatively more successful entrepreneurial coworkers (i.e., entrepreneurs whose firms were relatively large or productive). While spillovers exist in most sectors, they are largest in the accommodation and food service sector, suggesting that the aggregate evidence of spillovers are not driven by the “high tech" sector that previous literature has studied….Additionally, these spillovers are stronger for younger individuals and do not exist for near-retirement individuals, with individuals learning" the most from their relatively older coworkers. The spillovers are strongest across coworkers who may be more likely to regularly interact in the workplace or to form mentorship relationships, namely coworkers who earn similar wages or who are of the same sex or immigration status. Finally, spillovers generate new entrepreneurs: individuals who themselves have recent entrepreneurial experience are not more likely to become entrepreneurs after working with more entrepreneurial coworkers, consistent with these individuals having had their own experiences and thus little to learn from entrepreneurial coworkers….”

Her basic story, “…I study the entrepreneurial outcomes of the individuals who become entrepreneurs between 2005 and 2009 and find evidence of both spillovers of institutional knowledge and entrepreneurial skill. I estimate regressions models of whether an individual's future entrepreneurial firm's characteristics vary if they were exposed to more entrepreneurial coworkers in 2004. I find that individuals who work with more entrepreneurial coworkers tend to start firms that are smaller in both employment and sales and are less likely to survive, consistent with a net pattern of individuals on average simply being inspired or learning the institutional knowledge needed to start a firm, as this leads to less productive individuals choosing to become entrepreneurs. However, if the individuals' entrepreneurial coworkers ran larger or longer-surviving firms, the individuals are more likely to start firms that are larger and more likely to survive. These results suggest scope for some true productivity gains via entrepreneurial skill spillovers, if the spillovers are from particularly successful entrepreneurs…”

Controls that address your concern, “… Coworkers are not randomly assigned to individuals, creating identification concerns, despite the rich set of controls included in the baseline specifications. For instance, entrepreneurship-prone individuals may cluster at firms or establishments for a variety or reasons not captured by the observable controls, such that the spillovers I measure may not be evidence of learning from coworkers. Instead, these spillovers may reflect firm or establishment effects, consistent with the literature of entrepreneurs “spawning" from their employers. Or, these spillovers may reflect pure spurious correlation, if these entrepreneurship-prone individuals work at firms for reasons entirely unrelated to entrepreneurship, for instance if they are also more educated. Additionally, individuals living and working in particular locations or industries may experience common business shocks that make entrepreneurship more or less attractive. I address these identification concerns through several robustness analyses. First, I show that these spillovers do not reflect entrepreneurial individuals simply clustering at certain firms or establishments. Instead, individuals disproportionately appear to learn from their true coworkers other workers at their establishment as opposed to other workers at the same firm but at other establishments or workers who work at the same establishment either before they joined or after they leave.Second, I show that these spillovers cannot be fully accounted for by selection into having specific coworkers; the results are not driven by individuals seeking out entrepreneurial coworkers. Instead, spillovers are also apparent from coworkers who joined an individual's establishment after they joined, whom the individual should not have been able to select on when choosing to join the firm. Finally, spillovers are not driven by local or industry common shocks, as the patterns persist with the inclusion of additional location and industry fixed effects. To further bolster a causal interpretation of the spillovers, I present direct survey evidence of entrepreneurial spillovers across coworkers. By linking individuals who become entrepreneurs to firm owners in a Census survey, I analyze reported motivations for these individuals' entrepreneurship. I find that the individuals who previously worked with more former entrepreneurs are more likely to report that they had an entrepreneurial role model who led them to start a firm. In other words, the individuals who I predict become entrepreneurs because of their exposure to entrepreneurial coworkers are more likely to report having an entrepreneurial role model, consistent with these spillovers actually existing…”

She doesn’t explicitly address that question but she does implicitly address it (in her words her goal)“…I find that if an individual interacts more with former entrepreneurs, then they are more likely to become an entrepreneur subsequently. I compare the future entrepreneurship of individuals who work with more coworkers who were recently entrepreneurs to those who work with proportionally fewer entrepreneurial coworkers. Effectively, I want to compare individuals who are very similar, both in terms of their own demographics and entrepreneurship experience and their current firms, but who have different exposure to entrepreneurial experience….”