Macro Roundup

Potentially useful quote. Summers on how he squares his concerns about secular stagnation with opposition to the size of Biden's proposal, "...I remain concerned, as a medium-term worry, with secular stagnation, believe that fiscal policy will need to be much more active in the years ahead, and certainly share the administration’s view that policy should err very much on the side of expansion at a moment like this. But these kinds of qualitative considerations do not provide a basis for judging whether $900 billion in short-term stimulus should be followed immediately by a $1 trillion, $1.9 trillion or $5 trillion measure, prior to an ultimate multitrillion-dollar public investment measure. The issue policymakers have to wrestle with is quantitative. In my piece, I suggested that a 13 percent of GDP total stimulus (adding the $900 billion and the $1.9 trillion) was very large, especially in an economy with extraordinarily loose financial conditions, reasonably rapid growth forecasts, still unmet public spending needs and a very big overhang of private saving. Budget deficits in 2021 on the proposed plans will quickly be approaching the record World War II levels as a share of the economy.As the debate over stimulus plays out, it will be important to consider analyses of the proposed level of stimulus that can be delivered without setting off inflationary pressures over the next few years — especially if major new building-back-better investments are contemplated.I agree wholeheartedly with CEA member Jared Bernstein that balancing risks associated with insufficient and excessive stimulus is essential, and that it is right, given the lessons learned from the political choices made in 2008, to err this time on the side of doing too much. But 13 percent of GDP with public investment to follow needs to be justified with more than qualitative argument.Given that for example the International Monetary Fund forecasts 5.1 percent GDP growth for the United States in 2021 the proposed fiscal program seems extremely generous, but perhaps it can be justified in the debates that are ahead...."

Larry Summers, "My column on the stimulus sparked a lot of questions. Here are my answers.,"Washington Post, February 7, 2021, https://www.washingtonpost.com/opinions/2021/02/07/my-column-stimulus-sparked-lot-questions-here-are-my-answers/

Ed Noted, “Estimates by Harvard economics professor Raj Chetty and his colleagues suggest that consumer spending by low-income consumers is up more than 13 percent from January 2020 to January 2021, before any new stimulus.”

Justin Fox on the IRS numbers the Tax Foundation looked at, "...Those at the very top of the income distribution got tax cuts that were, as a percentage of income, not all that different from the middle to lower rungs. (Yes, there were some weird things going on below $10,000, but they can be chalked up to the vagaries of who files sub-$10,000-income tax returns, such as dependents with investment income.)... A different IRS data set that divides taxpayers near the top of the income distribution by percentile and even thousandth of a percentile allows us to pinpoint the greatest beneficiaries even more precisely. It was those between the 98th and 99th percentiles, with adjusted gross incomes ranging from $359,368 to $540,009 (mid-point: $449,689), who got the biggest tax cut.The main reason this group benefited so much is that the authors of the 2017 tax legislation took a chainsaw to the Alternative Minimum Tax. The AMT was born in 1969 as a fall-back tax system meant to snare a few hundred people near the top of the income distribution who had been paying little or no tax. But because it wasn’t indexed to inflation, by the mid-2000s it was affecting millions of taxpayers...It’s the HENRYsin less expensive, low-tax states that got the biggest tax breaks, and the pandemic-induced shift to remote work could mean there will be more of them in the years to come….”

Interesting analysis from Cleveland Fed on the pandemic's impact on urban migration. Net there has been outmigration from cities, largely by younger and higher income renters. However this effect has been driven primarily because of a decline in people moving to the cities (which may mean revert once pandemic is over.)

Core finding, “…As measured by net migration to and from urban neighborhoods, 2020 does appear to have seen an urban exodus relative to the migration flows during the preceding 10 years (Figure 1). The net flow of people out of US urban neighborhoods averaged nearly 28,000 people per month in March through September of the recent years 2017 to 2019. That number about doubled—to 56,000 people per month—in 2020 after the pandemic’s onset in March. Figure 2 shows that the decline of in-migration is actually the larger driver. The average of monthly out-migration was 276,000 in March through September 2020. This is 10,000 more than its average of 266,000 for the same months in 2017 through 2019. The larger change was for in-migration, which fell 18,000—from an average of 238,000 in 2017 to 2019—to 220,000 in 2020. Figure 2 shows the four-quarter moving average of the estimated flows to smooth out seasonality. Doing so also balances the dip and spike that occurred because the pandemic delayed many moves from the spring into the summer….”

For tracts that have a majority pre-WWII housing, I also require that they have a population density of at least 2,000 people per square mile. This avoids including exurban edges where old rural homes have not yet been outnumbered by homes in new subdivisions. I selected the 7,000 people per square mile threshold because it was the average density of major cities in the pre-WWII period.

Brevoort, Kenneth P., Philipp Grimm, and Michelle Kambara. 2016. “Credit Invisibles and the Unscored.” Cityscape: A Journal of Policy Development and Research. 18 (2).https://www.huduser.gov/portal/periodicals/cityscpe/vol18num2/ch1.pdf.

Cullen, Julie Berry, and Steven D. Levitt. 1999. “Crime, Urban Flight, and the Consequences for Cities.” The Review of Economics and Statistics. 81(2): 159-169.https://doi.org/10.1162/003465399558030.

Did the COVID-19 Pandemic Cause an Urban Exodus?

One constant through the upheavals of 2020 was the steady stream of media reports about residents’ fleeing dense urban areas.1 In this data brief, I use the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP) and find that migration flows were in fact very unfavorable for urban neighborhoods in 2020. However, people’s taking flight from urban areas is only part of the story.

Initially, the urban exodus stories reported that people were afraid of contracting the novel coronavirus in elevators and subways. Then the narratives suggested remote work had freed office workers from long commutes, allowing them to relocate. With both remote workers and students at home full time, a desire for home offices purportedly rose, and low interest rates made buying a larger suburban home attractive. Urban amenities such as restaurants and theaters were shuttered. Later, the protests sparked by the killing of George Floyd and others were cited for motivating some urban residents to leave. Most major cities experienced increases in violent crime during 2020, and crime rates have historically predicted migration changes.2 The proposed and enacted cuts to police funding were also cited as a reason to leave by some people who feared that crime would increase further.

The media reports have used a variety of data sources to measure the urban exodus, ranging from real estate listings to the hiring of movers and renting of moving vans. For this District Data Brief, I use the CCP, which is arguably a more accurate source for measuring migration. The CCP is a nationally representative anonymous random sample of 5 percent of US consumers with a credit file, resulting in a sample of more than 10 million adults. Approximately 9 of 10 adults have credit records, so CCP-based estimates are more representative than measures that reflect only homebuyers or only clients of a specific company. The populations the CCP underrepresents include low-income people who do not use traditional lenders (for example, banks and credit cards) and college-aged adults whose parents are still handling their finances.3 The techniques and definitions I use in this brief are the same as those I detailed in two 2019 Economic Commentaries (Whitaker, 2019a and 2019b).

Lenders report their borrowers’ current addresses to Equifax each month, and the CCP reports each borrower’s census tract. Using census tracts allows us to observe moves into and out of urban neighborhoods with much more precision than the many data analyses that rely on county-level migration aggregates. Almost all counties include both high-density and low-density neighborhoods. In the CCP, we can observe urban-suburban transitions that do not cross a county line, and we can distinguish those transitions from suburb to suburb moves that do cross a county line. I define urban neighborhoods as census tracts in metro areas with populations of more than 500,000 that have either a population density of more than 7,000 people per square mile or the majority of their housing stock built before World War II (WWII).4 Having either of these characteristics makes it more likely the neighborhood will be recognizable as urban. Neighborhoods built before WWII were designed for pedestrians, so residential, commercial, and retail buildings are within walking distance. Very-high-density neighborhoods built after WWII can also offer an urban lifestyle if they can support walkable retail and high-frequency public transit. In nonurban neighborhoods, zoning usually separates residences from businesses, so almost all trips require a car.

As measured by net migration to and from urban neighborhoods, 2020 does appear to have seen an urban exodus relative to the migration flows during the preceding 10 years (Figure 1). The net flow of people out of US urban neighborhoods averaged nearly 28,000 people per month in March through September of the recent years 2017 to 2019. That number about doubled—to 56,000 people per month—in 2020 after the pandemic’s onset in March.

The correlations run in the direction we would expect for three of the relationships. Net out-migration was greater for metro areas that had more deaths from COVID-19 and those with more telework-capable occupations. Metro areas that had more small businesses remain open experienced less net out-migration. The increases in homicides in the metro area’s central city was not associated with increased net out-migration.

Did the COVID-19 pandemic start an urban exodus? The estimates presented here strongly suggest that migration flows were unfavorable for urban neighborhoods during 2020. However, the word “exodus” emphasizes that migrants are leaving a particular area in large numbers. If we look at the gross flows underlying the trends in Figures 3 through 6 (available in the appendix), we can see that the declines of in-migration are almost always greater than the increases in out-migration. Out-migration did increase in many urban neighborhoods, but the magnitudes probably would not fit most definitions of an exodus. What is certain is that hundreds of thousands of people who would have moved into an urban neighborhood in a typical year were unwilling or unable to do so in 2020. These people may be harder to identify, label, and interview, but they may be best positioned to tell the real story.

Footnotes

In the appendix, I provide 16 examples of these articles.

Stephan Whitaker, "Did the COVID-19 Pandemic Cause an Urban Exodus?," Federal Reserve Bank Of Cleveland, February 5, 2021, https://www.clevelandfed.org/newsroom-and-events/publications/cfed-district-data-briefs/cfddb-20210205-did-the-covid-19-pandemic-cause-an-urban-exodus.aspx

Takeaway, “….Did the COVID-19 pandemic start an urban exodus?The estimates presented here strongly suggest that migration flows were unfavorable for urban neighborhoods during 2020.However, the word “exodus” emphasizes that migrants are leaving a particular area in large numbers. If we look at the gross flows underlying the trends in Figures 3 through 6 (available in the appendix),we can see that the declines of in-migration are almost always greater than the increases in out-migration. Out-migration did increase in many urban neighborhoods, but the magnitudes probably would not fit most definitions of an exodus….”

The out migration was driven by younger, richer, renters, “…Figure 3 shows the net out-migration by neighborhood income, renter or homebuyer status, resident age, and population. For neighborhood income, I divide the flows by whether the urban neighborhood had above- or below-median household income. In most years, the net flows out of below-median-income neighborhoods are larger than the net flows out of above-median-income urban neighborhoods, but this pattern reversed in 2020. The net outflow of people who are purchasing a home at their destination neighborhood increased 62 percent, but the net outflow of those renting at their destination increased by 210 percent. By age category, young adults exhibited the largest increase in net outflows. The most populous metro areas, those with more than 5 million residents, saw larger increases in net outflows than did smaller metro areas…”

Prior to pandemic, “…in contemporary America, the overwhelming majority of prime-age male NILFs are NEETs: in the years 2015-19, according to Census Bureau data, fewer than one in six NILFs was an adult student. In the lead-up to the COVID pandemic, this meant one in 10 prime-age men was neither working, nor looking for work, nor seeking the skills that might help them return to the workforce….”

How prime age men not in labor force and also not in school or job training spend their days, "...If we disaggregate prime-age NILFs into NEETs and adult students, two strikingly different ways of life are revealed. On the one hand, adult students reportedly spend an average of nearly six hours a day on their education or training—and since those averages include weekends and holidays, these men are committing over 2,100 hours a year to their schooling. The converse of such motivation is an unusually low involvement in “socializing, relaxing, and leisure”—distinctly less than for working men, though not as little as for prime-age working women, a notoriously “leisure-poor” population. On the other hand, self-identified prime-age NEET men spend about seven and a half hours a day in “leisure activities.”2 That works out to about 2,700 hours a year—almost 1,600 hours a year more than working women, nearly 1,400 hours a year more than working men, and remarkably enough, over 450 hours a year more than unemployed men…”
This leisure time is largely spent on digital devices (in terms of screens) and for a good many of them on drugs"...NEET men reported an average of over five hours a day in front of screens—nearly 1,900 hours a year, almost equivalent to the time commitment of a full-time job. ATUS does not ask specifically about video games; if it did, even more NEET screen time commitment would almost certainly be recorded. To go by the time-use surveys, prime-age men without work who are not looking for jobs and not engaged in training spend almost three times as many hours in front of screens as working women and well over twice as many as working men.3Strikingly, they also report over 300 hours more screen time per year than their unemployed counterparts—men likewise jobless but who want to get back to work. And the reality is even more disturbing than these time-use numbers can convey on their own. According to a 2017 study by Alan Krueger, almost half of NILF men reported taking some form of pain medication every day. The fraction for NEET men would likely be higher still. The rhythms of life for a great many of the prime-age men in America currently disengaged with the world of work is defined not simply by days and nights sitting in front of screens—but sitting in front of screens while numbed or stoned...."