Macro Roundup

Greg Mankiw footnotes his recent NYT op-ed, "...economists disagree about the how far the tax-based explanation goes. A reasonable reading of the literature is that lower labor effort and incomes in Europe are likely due to a combination of higher tax rates, stronger unions, and greater regulations......we find that the divorce and tax mechanisms together can explain 45% of the variation in labor supply between the United States and the European countries."......Europeans today work much less than Americans because of the policies of the unions in the 1970s, 1980s, and part of the 1990s and because of labor market regulations. Marginal tax rates may have also played a role......Based on our reading of the micro evidence, we recommend calibrating macro models to match Hicksian elasticities of 0.3 on the intensive and 0.25 on the extensive margin and Frisch elasticities of 0.5 on the intensive and 0.25 on the extensive margin. Hence, it would be reasonable to calibrate representative agent macro models to match a Frisch elasticity of aggregate hours of 0.75. These elasticities are consistent with the observed differences in aggregate hours across countries with different tax systems..."

Follow-up references

In my most recent Times column, I did not have the space to fully explain the body of work that follows up on the Prescott hypothesis that higher tax rates explain lower work effort and national incomes in Western Europe. For interested readers (that is, the more nerdy ones), here are a some relevant references together with brief excerpts (emphasis added):

1. Steven Davis and Magnus Henrekson

"Lastly, let us return to the recent studies by Prescott (2002, 2003), which consider the output, employment and welfare consequences of personal taxes in an equilibrium model with one production sector and a simple labor-leisure choice for the representative household. Our evidence supports the view that tax rate differences among rich countries are a major reason for large international differences in market work time. At the same time, however, our evidence strongly suggests that labor and consumption taxes operate with powerful effect on several margins: substitution between legal and underground activity, substitution between home and market production, the mix of market production activity, and the composition of market expenditures."

2. Indraneel Chakraborty et al.

"Americans work more than Europeans. Using micro-data from the United States and 17 European countries, we document that women are typically the largest contributors to the cross-country differences in work hours. We also show that there is a negative relation between taxes and annual hours worked, driven by men, and a positive relation between divorce rates and annual hours worked, driven by women. In a calibrated life-cycle model with heterogeneous agents, marriage and divorce, we find that the divorce and tax mechanisms together can explain 45% of the variation in labor supply between the United States and the European countries."

3. Alberto Alesina et al.

"Our punch line is that Europeans today work much less than Americans because of the policies of the unions in the 1970s, 1980s, and part of the 1990s and because of labor market regulations. Marginal tax rates may have also played a role, especially for women's labor force participation, but our view is that in a hypothetical competitive labor market without unions and with limited regulation, these tax increases would not have affected hours worked as much. Certainly micro evidence on the elasticity of labor supply is inconsistent with a mainly tax-based explanation of this phenomenon, even though social multiplier effects may help in this respect."

4. Raj Chetty et. al.

"Based on our reading of the micro evidence, we recommend calibrating macro models to match Hicksian elasticities of 0.3 on the intensive and 0.25 on the extensive margin and Frisch elasticities of 0.5 on the intensive and 0.25 on the extensive margin. Hence, it would be reasonable to calibrate representative agent macro models to match a Frisch elasticity of aggregate hours of 0.75. These elasticities are consistent with the observed differences in aggregate hours across countries with different tax systems."

As I noted in my column, economists disagree about the how far the tax-based explanation goes. A reasonable reading of the literature is that lower labor effort and incomes in Europe are likely due to a combination of higher tax rates, stronger unions, and greater regulations.

Greg Mankiw, “Follow Up References,” Greg Mankiw’s Blog, September 22, 2021, http://gregmankiw.blogspot.com/2021/09/follow-up-references.html

Justin Fox takes a snapshot of autumn 2021 in New York, "...Payroll employment in the city fell 19.5% in the first three months of the pandemic and isstill down almost 11% from February 2020 on a seasonally adjusted basis. Nationwide the decline is now just 3.5%, and a not-seasonally-adjusted long view (seasonally adjusted numbers are only available back to 1990) shows how massive the recent hit to New York has been from a historical perspective.... Weekday subway ridership in New York City, meanwhile, is now at a little over 50% of the pre-pandemic norm....At present, visits to retail and recreational establishments in Manhattan (a city-wide estimate for New York is not available) are still down more than 60% from the pre-pandemic norm, a decline similar to that in European cities but much bigger than in Hong Kong or Mexico City....OpenTable’s statistics on seated diners at the restaurants using its reservation service, meanwhile, show New York about even with San Francisco near the bottom of the pack....he picture is even worse for the city’s lodging industry, with a recent report from the American Hotel & Lodging Association and Kalibri Labs projecting an 88.4%, decline in business travel revenue for New York in 2021 relative to 2019.... In the weekly office occupancy statistics maintained by Kastle Systems, New York was ahead of only San Francisco and San Jose with office security-card swipes at 28.1% of the pre-pandemic norm in the week of September 15.... Zumper’s asking-price data, meanwhile, shows the median one-bedroom rent in New York hitting $2,810 in August, pushing it past San Francisco as the country’s most expensive market. These rising rents are a sign that confidence in the city’s future is returning, but they’re also somewhat tragic, given that most New Yorkers can’t afford $2,810 one-bedroom apartments...."

Justin Fox, "Is New York Back? Crime and Jobs Data Point to a Long Recovery," Bloomberg, September 23, 2021, https://www.bloomberg.com/opinion/articles/2021-09-23/new-york-city-is-still-a-shell-of-its-former-self

Is New York Back? Crime and Jobs Data Point to a Long Recovery

One could get the impression from reading headlines these days that New York City is back. Landlords are jacking up apartment rents by as much as 70% from pandemic lows. Google just said it would buy a downtown Manhattan building for $2.1 billion, the biggest office transaction in the U.S. since Covid-19 hit. Subway ridership is reaching new pandemic-era highs. And so on.

Indeed, the city has come far from the awful days of spring 2020. By a few metrics — apartment rents, mainly — it really is back to where it was before the pandemic. By most others, though, it still falls miles short of a full recovery.

I write this, I might as well admit, partly in response to my San-Francisco-based Bloomberg Opinion colleague Noah Smith’s claim last week that New York “is emerging as one of the world’s most resilient big cities in the wake of the pandemic.” Sure, it looks pretty lively in comparison with San Francisco, where hardly anybody has gone back to the office and tourist zones like the area around Union Square remain deserted (or at least were when I was there a few weeks ago). It also isn’t spiraling ever downward in a repeat of the terrible 1970s, as some feared last summer. There are even tantalizing hints that it may come back as something better than it was — at least that’s what I told myself while biking home at dusk on Sunday after dropping off a U-Haul truck in East Harlem (long story) and encountering scene after lovely scene of New Yorkers dining outdoors, playing and socializing on closed-off streets, and getting around on two-wheeled vehicles of many varieties.

New York City is going to have trouble making much more headway, though, without a far bigger jobs recovery than it has seen so far. Payroll employment in the city fell 19.5% in the first three months of the pandemic and is still down almost 11% from February 2020 on a seasonally adjusted basis. Nationwide the decline is now just 3.5%, and a not-seasonally-adjusted long view (seasonally adjusted numbers are only available back to 1990) shows how massive the recent hit to New York has been from a historical perspective.

Weekday subway ridership in New York City, meanwhile, is now at a little over 50% of the pre-pandemic norm. That’s much higher than Bay Area Rapid Transit (in San Francisco and environs) and the Washington Metro have seen so far, an indication of how much more reliant New Yorkers are on public transportation. But it’s about the same as what the London Underground is experiencing and well short of where the Paris Metro and commuter-rail systems were early in the summer.

Google’s community mobility statistics offer another way to compare how much people are getting out and about around in the world. At present, visits to retail and recreational establishments in Manhattan (a city-wide estimate for New York is not available) are still down more than 36% from the pre-pandemic norm, a significantly bigger decline than in Hong Kong, Berlin, Mexico City or Amsterdam.

Apartment rents are of course another matter, with Apartment List’s estimate of the median rents paid by recent movers showing New York’s surpassing pre-pandemic levels for the first time in August. That’s in contrast to San Francisco and Washington, where rents still haven’t fully recovered. On the other hand, rents in the Florida coastal city of Boca Raton have gone from 11% lower than New York’s to 7% higher over the course of the pandemic.

These rising rents are a sign that confidence in the city’s future is returning, but they’re also somewhat tragic, given that most New Yorkers can’t afford $2,810 one-bedroom apartments. Unlike San Francisco and its suburbs (and even more, the San Jose-Sunnyvale-Santa Clara metro area just to its south), New York is not especially affluent. The city’s median household income has been barely higher than the nation’s in recent years, and probably dropped below it in 2020. There were indications that high real estate prices were driving people away from the city before the pandemic hit, and while New York’s surprisingly high population count in the 2020 Census was taken by some as evidence that earlier Census estimates showing a population decline starting in 2016 were wrong, I think that’s a misreading: the population decline may well have been real, just starting from a higher base than thought.

The spasm of violent crime that began last summer is another problem. It’s worse in other places than in New York, where murders are now trending downward and shootings have stopped rising relative to last year, but the city remains more dangerous than it was before the pandemic. It’s also gotten a lot dirtier because of Sanitation Department cutbacks and worker shortages — which again are not unique to New York but are an especially big challenge here. Finally, while tax collections have come in a lot stronger than forecast last year and early this year, they’re still running billions short of what the city will need to pay its bills when federal pandemic aid runs out.

The 20th anniversary of the Sept. 11, 2001 terrorist attacks provided an opportunity to marvel at New York’s past resilience. In the immediate aftermath of 9/11 the city seemed permanently damaged. Some speculated that big cities and tall office buildings would go out of fashion. Instead, the country’s biggest city came roaring back.

Will it do so again? Yeah, I think so. But Covid-19 has killed many more New Yorkers and caused far more economic damage than 9/11 did, and the remote-work revolution it spawned has already been more wide-reaching and durable than post-9/11 tall-building-phobia ever was. It’s going to be a long, hard road to recovery for New York, and acting like we’re already there seems like a big mistake.

Anuraag Singha, Giorgio Triulzib and Christopher Magee, “Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description, Research Policy, https://www.sciencedirect.com/science/article/pii/S0048733321000950

Software is eating the world (or at least improving more rapidly than other sectors), “…While most domains are indeed slow, we also find some very fast domains in the dataset. Table 2 identifies the 10 fastest improving domains. The domains are identified by the Domain code- a portmanteau of the UPC code and IPC code of the underlying overlapped classes. The predicted improvement rate (predicted K) i.e. the values from Eq. (2) are also given along with the domain size i.e. the number of patents (after deduplication). The predicted rates of improvement are reported as percentage change per annum. Table 2 shows very little diversity among technologies with most encompassing software improvements as an essential part of the domain. Interestingly, there is also little diversity among the slowest domains. The slowest domains are mostly related to simple mechanisms such as hitches, gauges, batons, hangers, cowls, hammocks etc. Please see Appendix H for the list of identified fastest 20 and slowest 20 domains…”

Improvement rates are not uniformly distributed across and within categories,”…Fig. 3 reports the distribution of domains by category, broken down, for each category, by quartile of the overall distribution of improvement rates across all domains. If improvement rates would be uniformly distributed across categories and within categories, we would observe that each quartile bar would have the same height (i.e., same number of domains) for all categories. In other words, we would see 24 bars of the same height. In contrast, we see very large differences in number of domains across quartiles within categories. Computer & Communications domains are predominantly found in the fastest improving quartile of the overall distribution. Similarly, Electrical & Electronics domains are mostly found in the top two quartiles. Chemical domains are concentrated in the middle two quartiles, while Mechanical and Others are mostly found in the slower quartiles. Drugs & Medical domains are more uniformly distributed across all quartiles, meaning that there is roughly equal probability of finding fast and slow domains in this category….”

Most technologies improve moderately or slowly, “…More than 83% of the technological domains are improving at a rate of less than 25% per annum and more than two-thirds of the domains are improving at a rate less than 15% per annum. Performance for the average technology improves at a rate of 19% per annum, with a standard deviation of 26% per annum. Fig. 2 shows the distribution of the predicted improvement rate values for the 1757 domains and the best fit for the probability density function. The distribution is very skewed and its best fit is log-normal with shape, location and scale parameters respectively equal to 0.95, 1.92 and 9.90….”

Core findings

Using evidence from patents a paper finds technology seems to improve at a steady state, “…In this work, we provide predicted yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of 1757 technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. These domains contain 97.2% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to predict their improvement rates, following a patent network-based methodology tested in prior work. They vary from a low of 2% per year for the Mechanical Skin treatment- Hair Removal and wrinkles domain to a high of 216% per year for the Dynamic information exchange and support systems integrating multiple channels domain, but more that 80% of technologies improve at less than 25% per year. Fast improving domains are concentrated in a few technological areas. The domains that show improvement rates greater than the predicted rate for integrated chips, from Moore’s law, are predominantly based upon software and algorithms. In addition, the rates of improvement were not a strong function of the patent set size….”