Category: Data Source

The Fast, the Slow, and the Congested

by  Tyler Cowen in

That is a new NBER working paper by Protty A. Akbar, Victor Couture, Gilles Duranton, and Adam Storeygard.  Here is the abstract:

We assemble a new global database on motor vehicle travel speed in over 1,200 large cities in 152 countries. We then estimate comparable city-level indices of travel speed and congestion. Most of the variation in urban travel speed is across countries, not within. National income per capita explains most of this cross-country variation in speed. In rich countries, urban travel is roughly 50% faster than in poor countries. To investigate the link between economic development and mobility, we develop an urban model with endogenous travel, road infrastructure, and land area. The model provides an exact decomposition of how city size, infrastructure, and topography contribute to explaining why urban travel is faster in richer countries. We find that richer countries are faster, mainly because their cities have more major roads and wider land areas. These effects operate by increasing uncongested speed, not by reducing congestion.

In general, I am much more skeptical about slower modes of transportation than are many other MR readers, most of all those on the Left.

If you look on p.36, “major road length” is the variable most predictive of speed if you ask why rich countries are faster than are poor countries — please do not forget the Lucas Critique however!  After all, they do measure Flint, Michigan as the fastest city [sic] in the world.

Here is a good paragraph (p.37):

Compared to other cities in the oecd, us cities are (exp(−0.27) − 1 =) 24% less populous, cover 72% more area, have 67% more major roads, and have 30% more roads that conform to the road network’s main grid orientation. Panel A of figure 5 reports the corresponding decomposition result. The low density of us cities explains most of why they are faster. Major roads matter too, as do griddier road networks. City size variables account for 47% of the speed difference between the us and other oecd countries, infrastructure accounts for 35%, and the share accounted for by topography is negligible.

If you are wondering, by their measures Dhaka is the slowest city in the world.

An excellent paper, recommended.

The impacts of Covid-19 absences on workers

by  Tyler Cowen in

In the Journal of Public Economics, by Gopi Shah Goda and Evan J. Soltas:

We show that Covid-19 illnesses and related work absences persistently reduce labor supply. Using an event study, we estimate that workers with week-long Covid-19 absences are 7 percentage points less likely to be in the labor force one year later compared to otherwise-similar workers who do not miss a week of work for health reasons. Our estimates suggest Covid-19 absences have reduced the U.S. labor force by approximately 500,000 people (0.2 percent of adults) and imply an average labor supply loss per Covid-19 absence equivalent to $9,000 in forgone earnings, about 90 percent of which reflects losses beyond the initial absence week.

Here is the full article.

Law-Abiding Immigrants

by  Tyler Cowen in

The subtitle is The Incarceration Gap Between Immigrants and the U.S.-Born, 1850–2020, and the authors are Ran Abramitzky, Leah Boustan, Elisa Jácome, Santiago Pérez, and Juan David Torres.  Here is the to-the-point abstract:

Combining full-count Census data with Census/ACS samples, the researchers provide the first nationally representative long-run series (1870–2020) of incarceration rates for immigrants and the U.S.-born. As a group, immigrants had lower incarceration rates than the US-born for the last 150 years. Moreover, relative to the U.S.-born, immigrants’ incarceration rates have declined since 1960: Immigrants today are 60% less likely to be incarcerated (30% relative to U.S.-born whites). This relative decline occurred among immigrants from all regions and cannot be explained by changes in immigrants’ observable characteristics or immigration policy. Instead, the decline likely reflects immigrants’ resilience to economic shocks.

Here is the full paper, via Anecdotal.

The Dominican Republic is underrated

by  Tyler Cowen in

Despite being one of Latin America’s poorest countries in the mid-1960s, the Dominican Republic has made remarkable progress in terms of income convergence…

What is remarkable about the Dominican Republic’s progress is not just the level of convergence but also its speed compared to other countries in the region. By examining the average convergence velocity, or the rate of change in income convergence per decade, it is evident that the Dominican Republic has exhibited the highest average convergence velocity, or “blue shift,” in Latin America over the past 50 years. Panama and Chile have achieved equally meaningful but still lower positive convergence velocities, while the majority of countries in the region have experienced either very low (“green shift”) or negative (“red shift”) convergence velocities.

Here is the full IMF brief, retweeted by Matt Yglesias.  There is much more at the link.  I have been there twice, including as recently as last year (albeit briefly), and this accords with my intuitions.

Who Runs the AEA?

by  Tyler Cowen in

That is a new JEL publication (gated) by Kevin D. Hoover and Andrej Svorenčík, here is the abstract:

The leadership structure of the American Economic Association is documented using a biographical database covering every officer and losing candidate for AEA offices from 1950 to 2019. The analysis focuses on institutional affiliations by education and employment. The structure is strongly hierarchical. A few institutions dominate the leadership, and their dominance has become markedly stronger over time. Broadly two types of explanations are explored: that institutional dominance is based on academic merit or that it is based on self-perpetuating privilege. Network effects that might explain the dynamic of increasing concentration are also investigated.

And this:

The current paper is based on an extensive prosopographical database covering the entire leadership of the AEA over the
1950–2019 period, including all Presidents, Presidents-elect, Vice Presidents, ordinary members of the Executive Committee, as well as the losing candidates for all elective offices, and members of the Nominating Committee.

The results?:

The 14 institutions in the table account for almost more than 80 percent of the positions for the whole 1950–2019 period. Even within this select group, the distribution is highly skewed with Harvard, the top supplying institution over the period accounting for more than a fifth of the total, and the last five universities accounting for around 2 percent each. The top five institutions, Harvard, MIT, Chicago, Columbia, and Stanford, which we designate as the first tier, account for over half (57.1 percent) of the positions over the whole period…

The authors summarize their findings:

The most obvious lessons are, perhaps, hardly surprising: the AEA leadership is overwhelmingly drawn from a small group of elite, private research universities—in the sense that its leaders were educated at these universities and, to a lesser degree, employed by them. What is less well-known is that for much of the past 70 years, the AEA leadership has been drawn predominantly from just three universities—Harvard, MIT, and Chicago.

By the way, institutional concentration has become more pronounced over time, not less.  But since about eighty percent of U.S. students go to state schools, most of those large state schools, I guess we can reconfigure all these panels to have eighty percent state school representation, rather than 80 percent elite school representation.  Right?  Right?

You may or may not like these facts (I for one am willing to admit to more elitism than are many people), for the time being I will say only this: “Do not listen to what they say, watch what they do!”

South Appalachia > North Appalachia

by  Tyler Cowen in

The ARC classifies 27.2 percent of North Appalachian counties as distressed but only 9.6 percent of South Appalachian counties that way. Over 70 percent of counties in South Appalachia have grown in population since the 2020 Census. North Appalachia lost 17,131 people in total, while South Appalachia gained 127,585. The difference in net in-migration is even more stark. While the North posted positive net domestic in-migration of 22,563, the South tallied almost 300,000—13 times as high. The story is similar for jobs, with the North losing 227,049 positions since the pre-pandemic year of 2019, while the South actually exceeded its pre-Covid levels by 66,377. In other words, much of South Appalachia is seeing a population inflow and is growing in both population and employment.

Here is much more from Aaron M. Renn, of interest and with good maps, and for the pointer I thank Terry O’Connor.

The Relentless Rise of Stablecoins

by  Alex Tabarrok in

1. In 2022, stablecoins settled over $11tn onchain, dwarfing the volumes processed by PayPal ($1.4tn), almost surpassing the payment volume of Visa ($11.6tn), and reaching 14% of the volume settled by ACH and over 1% the volume settled by Fedwire. It is remarkable that in just a few years, a new global money movement rail can be compared with some of the world’s largest and most important payment systems.

2. Over 25mm blockchain addresses hold over $1 in stablecoins. Of these, ~80%, or close to 20mm addresses, hold between $1 and $100. For a sense of scale, a US bank with 25mm accounts would rank as the 5th largest bank in the US by number of accounts. The massive number of small-dollar stablecoin holdings indicates the potential for stablecoins to provide global financial services to customers underserved by traditional financial institutions.

3. Approximately 5mm blockchain addresses send stablecoins each week. This number provides a very rough proxy for global users regularly interacting with stablecoins. These ~5mm weekly active addresses send ~38mm stablecoin transactions each week, representing an average of over 7 weekly transactions per active address.

4. Stablecoin usage has decoupled from crypto exchange volumes, indicating that significant stablecoin transaction volumes may be driven by non-trading/speculative activity. Since December 2021, centralized exchange volumes are down 64%, and decentralized exchange volumes are down 60%. During this period, stablecoin volumes are down only 11%, and weekly active stablecoin addresses and weekly stablecoin transactions are up over 25%.

5. Of the ~5mm weekly active stablecoin addresses, ~75% transact less than $1k per week, indicating that small/retail users likely represent the majority of stablecoin users.

6. The outstanding supply of stablecoins has grown from less than $3bn five years ago to over $125bn today (after peaking at over $160bn) and has shown resilience to the market downturn with the market cap of stablecoins currently down ~24% from its peak, compared with a ~57% decline for the overall crypto market cap.

7. Less than 1/3rd of stablecoins are held on exchanges. Most are held in externally owned accounts (not exchanges or smart contracts).

8. The majority of stablecoin activity uses Tether (USDT). Tether represents 69% of stablecoin supply, and YTD has accounted for 80% of weekly active addresses, 75% of transactions, and 55% of volumes.

9. Most stablecoin activity occurs on the Tron and BSC blockchains. Year-to-date, the Tron and BSC blockchains collectively account for 77% of weekly active addresses, 75% of transactions, and 41% of volumes.

10. The Ethereum blockchain is used for higher value transactions (on average). Despite accounting for just 6% of active wallets and 3% of transactions, the Ethereum blockchain is home to 55% of stablecoin supply and settles close to 50% of weekly stablecoin $ volume.

These are all from a Bevan Howard report, The Relentless Rise of Stablecoins (requires email).

Market Response to Racial Uprisings

by  Tyler Cowen in

Defund the police was never really in the cards:

Do investors anticipate that demands for racial equity will impact companies? We explore this question in the context of the Black Lives Matter (BLM) movement—the largest racially motivated protest movement in U.S. history—and its effect on the U.S. policing industry using a novel dataset on publicly traded firms contracting with the police. It is unclear whether the BLM uprisings were likely to increase or decrease market valuations of firms contracting heavily with police because of the increased interest in reforming the police, fears over rising crime, and pushes to “defund the police”. We find, in contrast to the predictions of economics experts we surveyed, that in the three weeks following incidents triggering BLM uprisings, policing firms experienced a stock price increase of seven percentage points relative to the stock prices of nonpolicing firms in similar industries. In particular, firms producing surveillance technology and police accountability tools experienced higher returns following BLM activism–related events. Furthermore, policing firms’ fundamentals, such as sales, improved after the murder of George Floyd, suggesting that policing firms’ future performances bore out investors’ positive expectations following incidents triggering BLM uprisings. Our research shows how—despite BLM’s calls to reduce investment in policing and explore alternative public safety approaches—the financial market has translated high-profile violence against Black civilians and calls for systemic change into shareholder gains and additional revenues for police suppliers.

That is from a new NBER working paper by Bocar A. Ba, Roman Rivera, and Alexander Whitefield.

What’s Behind Her Smile? Health, Looks, and Self-Esteem

by  Tyler Cowen in

Looks matter!:

This paper examines how improving dental health affects economic, social, and psychological outcomes. In a randomized experiment, we provide a low-income group in Chile free dental care, including prostheses, and find significant and persistent impacts on men’s and women’s dental and self-perceived mental health. For women, treatment generates steady improvement in self-esteem, a higher likelihood of smiling when photographed, short-run improvements in employment and earnings, and improvement in partner interactions. We find no impact for men in any of these dimensions. Heterogeneity analyses suggest that treatment effects on labor market outcomes are larger for women with more severe visible dental issues at baseline. In summary, we find that increasing access to dental care, including cosmetic elements, improves important aspects of people’s lives.

That is from a forthcoming paper by Francisco A. Gallego, Cristian Larroulet Philippi, and Andrea Repetto.  Via Maxwell G.

Excess All-Cause Mortality in China After Ending the Zero COVID Policy

by  Tyler Cowen in

In this cohort study across all regions in mainland China, an estimated 1.87 million excess deaths occurred among individuals 30 years and older during the first 2 months after the end of China’s zero COVID policy. Excess deaths predominantly occurred among older individuals and were observed across all provinces in mainland China, with the exception of Tibet.

So what is the proper sarcastic headline here?  “I guess that flu was worse than we thought!”?  Or “How is it that China ran out of ivermectin?”  Here is the new JAMA piece, via Rich Dewey.

To be clear, I never thought Zero Covid was a sustainable policy for China.  The real criminal negligence lies with CCP leadership, which turned down opportunities to pursue joint mRNA vaccine production — with the West of course — earlier on.

Which businesses mix the classes best?

by  Tyler Cowen in

Casual restaurant chains, like Olive Garden and Applebee’s, have the largest positive impact on cross-class encounters through both scale and their diversity of visitors. Dollar stores and local pharmacies like CVS deepen isolation. Among publicly-funded spaces, libraries and parks are more redistributive than museums and historical sites. And, despite prominent restrictions on chain stores in some large US cities, chains are more class diverse than independent stores. The mix of establishments in a neighborhood is strongly associated with cross-class Facebook friendships (Chetty et al., 2022).

That is from a new paper by Maxim Massenkoff and Nathan Wilmers.  Via Scott Lincicome.

What makes for a good Royal Navy senior officer?

by  Tyler Cowen in

In most studies of talent, it is very difficult to get the top performers to respond or offer data.  This paper is a major exception to that general limitation:

This paper assesses the impact of general intelligence, as well as specific personality traits, and aspects of motivation, on performance, potential, and advancement of senior leaders. A questionnaire survey was conducted on the full population of 381 senior officers in the Royal Navy with an 80% response rate. Performance, potential, and rate of advancement were established direct from the organization’s appraisal system; intelligence, personality traits and motivation were assessed, at the time of the study, using the Verify G+ Test, Occupational Personality Questionnaire, and Motivation Questionnaire. Findings suggest differences in motivation are more important than differences in general intelligence, or personality traits, in predicting assessed performance, potential within, and actual rate of advancement to, senior leadership positions. This is a rare example of a study into very senior leaders, validated against both formal appraisal data and actual rates of advancement. As a consequence of this study the Royal Navy has started to use psychometric-based assessments as part of the selection and development of its most Senior Officers.

Here is the full (gated) paper by Mike Young and Victor Dulewicz.  I’ll pull out and repeat the key sentence there: “Findings suggest differences in motivation are more important than differences in general intelligence, or personality traits, in predicting assessed performance, potential within, and actual rate of advancement to, senior leadership positions.“