Category: Data Source
We find that immigration increases US life expectancy by 1.5 years for men and 1.4 years for women. Over half of these contributions occur at the prime working ages of 25–64. The difference between foreign-born and US-born mortality has grown substantially since 1990, with the ratio of US-born to foreign-born mortality rates nearly doubling by 2017. In that year, foreign-born life expectancy reached 81.4 and 85.7 years for men and women, respectively—7.0 and 6.2 years higher than their US-origin counterparts. These life expectancy levels are remarkable by most standards. Foreign-born male life expectancy exceeds that of Swiss men, the world leaders in male life expectancy. Life expectancy for foreign-born women is close to that of Japanese women, the world leaders in female life expectancy. The widening mortality difference between the US-born and foreign-born populations, coupled with an increase in the share of the population born abroad, has been responsible for much of the increase in national life expectancy in recent years. Between 2007 and 2017, foreign-born men and women were responsible for 44% and 60% of national life expectancy improvements. Between 2010 and 2017, immigrants experienced gains while the US-born experienced declines in life expectancy. Thus, nearly all of the post-2010 mortality stagnation is due to adverse trends among the US-born. Without immigrants and their children, national life expectancy in 2017 would be reduced to its 2003 levels. These findings demonstrate that immigration acts to bolster American life expectancy, with particularly valuable contributions at the prime working ages.
I will repeat what is to me the most striking excerpt:
Foreign-born male life expectancy exceeds that of Swiss men, the world leaders in male life expectancy. Life expectancy for foreign-born women is close to that of Japanese women, the world leaders in female life expectancy.
Many interesting results in there, for instance those immigrants sure are mighty! I strongly suspect much of that is selection, and another big part lifestyle, but yet another implication is that the U.S. health care system maybe isn’t as terrible as what you have been hearing. And that living in the U.S., over the generations, screws people up.
This paper presents evidence from parallel field experiments in China, Germany, and the United States. We contacted the mayor’s office in over 6,000 cities asking for information about procedures for starting a new business. Chinese and German cities responded to 36-37 percent requests; American cities responded to only 22 percent of requests. We randomly varied the text of the request to identify factors that affect the likelihood of receiving a response. American and German cities were more responsive to requests from citizens than foreigners; Chinese cities did not discriminate on this basis. Chinese cities were more responsive to requests from men than women; German cities did not discriminate on this basis and American cities had a slight bias in favor of women. Cities in all three countries were more responsive to requests associated with starting a construction business than a green business, but especially Chinese cities. Chinese cities were more responsive when the mayor was being considered for promotion than after a promotion decision, suggesting the importance of promotion incentives in China, but low responsiveness to green investment suggests limited incentives for environmental improvement. We argue that the response patterns are consistent with simple political economy theories of democracy and autocracy.
That is by Ekkehard A. Köhler, John G. Matsusaka, and Yanhui Wu. Via the excellent Kevin Lewis.
You may have seen the viral tweet suggesting that boomers own all the wealth and millennials are poor. It’s hard for me to get worked up. Talk about a problem that will solve itself!
The problem that the graph suggests, however, is not even correct. Why are we looking at generational wealth shares when we could be looking at the much more straightforward statistic, wealth per capita. Jeremy Horpedahl does just that:
Looking at the exact same data (from the Fed Distributional Financial Accounts) from a different perspective gives us a much different picture of recent history. In this version, Gen X is now richer (30% richer!) than Boomers were at the same age (late 40s). Millennials don’t yet have a year of overlap with Boomers, but they are tracking Gen X almost exactly. There is no reason they won’t continue to track Gen X, and therefore exceed Boomers as well when they are in their late 40s (which will happen in about 2037 for Millennials).
In other words, people in the current generation have as much or more wealth than people of previous generations did at the same age.
Read the whole thing if you want some additional astute points about student debt and politics but I call this one busted. The kids are doing alright.
Thus, to analysts, picking one such meta-analysis may feel as hard as picking a single “best study.” This paper responds by taking the meta-analysis another step, estimating a meta-analysis (or mixture distribution) of six meta-analyses. The baseline model yields a central VSL of $7.0m, with a 90% confidence interval of $2.4m to $11.2m. The provided code allows users to easily change subjective weights on the studies, add new studies, or change adjustments for income, inflation, and latency.
Many individuals travel between countries as part of their professional routines. How do they perform during those short trips abroad? To begin to answer this question, I analyzed the outcomes of over 5 million chess games played around the world. Importantly, tournament chess provides a clean setting in which location-dependent factors are mostly irrelevant; the audiences are quiet and the referees make hardly any judgments. Controlling for differences in chess skills, I found enhanced performance among players who were competing outside of their home countries. This finding was robust to additional controls such as age, sex, and skill momentum or game practice, and to the inclusion of individual or country fixed effects. This advantage, an approximately 2% increase in game outcome, suggests that traveling has a positive effect on performance.
Hispanics are slightly less likely to be jailed than whites…
A Council on Criminal Justice analysis found that in 2000, the rate of being on probation was 1.6 times higher and the rate of being parole was 3.6 times higher for Hispanics than non-Hispanic whites. But by 2016, the probation disparity had disappeared and the parole disparity had shrunk by 85%. Hispanics still faced a 60% higher risk of being incarcerated in a state prison. This is an enormous and worrying disparity, but the Council noted that it decreased by 60% since 2000…
The dwindling of Hispanic-white disparities is even more remarkable in light of criminal behavior being so heavily concentrated in adolescence and young adulthood,. The median age for Hispanics is 29.8 years versus 43.7 for whites, meaning even in a system free of prejudice that punished solely on the basis of crimes committed, we would expect criminal justice disparities between the populations to be growing, not shrinking.
That is from the Matt Yglesias Substack, but the actual writer is Keith N. Humphreys.
We examine the question of rationality, replicating two core experiments used to establish that people deviate from the rational actor model. Our analysis extends existing research to a developing country context. Based on our theoretical expectations, we test if respondents make decisions consistent with the rational actor framework. Experimental surveys were administered in Côte d’Ivoire and Ghana, two developing countries in West Africa, focusing on issues of risk aversion and framing. Findings indicate that respondents make decisions more consistent with the rational actor model than has been found in the developed world. Extending our analysis to test if the differences in responses are due to other demographic differences between the African samples and the United States, we replicated these experiments on a nationally representative analysis in the U.S., finding results primarily consistent with the seminal findings of irrationality. In the U.S. and Côte d’Ivoire, highly educated people make decisions that are less consistent with the rational model while low-income respondents make decisions more consistent with the rational model. The degree to which people are irrational thus is contextual, possibly western, and not nearly as universal as has been concluded.
Speculative, and not replicated, but the point remains of definite interest. Via the excellent Kevin Lewis.
Publishing in economics proceeds much more slowly on average than in the natural sciences, and more slowly than in other social sciences and finance. It is even relatively slower at the extremes. We demonstrate that much of the lag, especially at the extremes, arises from authors’ dilatory behavior in revising their work. The marginal product of an additional round of re-submission at the top economics journals is productive of additional subsequent citations; but conditional on re-submission, journals taking more time is not productive, and authors spending more time is associated with reduced scholarly impact. We offer several proposals to speed up the publication process. These include no-revisions policies; limits on authors’ time revising articles, and limits on editors waiting for dilatory referees.
Publishing takes a long time in economics. Consequently, many authors release “working” versions of their papers. Using data on the NBER working paper series, we show that the dissemination of economics research suffers from an overcrowding problem: An increase in the number of weekly released working papers on average reduces downloads, abstract views, and media attention for each paper. Subsequent publishing and citation outcomes are harmed as well. Furthermore, descriptive evidence on viewership and downloads suggests working papers significantly substitute for the dissemination function of publication. These results highlight inefficiencies in the dissemination of economic research even among the most exclusive working paper series and suggest large social losses due to the slow publication process.
Is less attention for each paper necessarily a bad thing?
When given the choice between a free meal and performing a task for a meal, cats would prefer the meal that doesn’t require much effort. While that might not come as a surprise to some cat lovers, it does to cat behaviorists. Most animals prefer to work for their food — a behavior called contrafreeloading.
A new study from researchers at the University of California, Davis, School of Veterinary Medicine showed most domestic cats choose not to contrafreeload. The study found that cats would rather eat from a tray of easily available food rather than work out a simple puzzle to get their food.
“There is an entire body of research that shows that most species including birds, rodents, wolves, primates — even giraffes — prefer to work for their food,” said lead author Mikel Delgado, a cat behaviorist and research affiliate at UC Davis School of Veterinary Medicine. “What’s surprising is out of all these species cats seem to be the only ones that showed no strong tendency to contrafreeload.”
Here is the link, via E Durbrow. Having grown up with multiple cats, I can attest that part of the results should come as no surprise. But why do other animals prefer to work for their food?
Kevin Lewis has been on such a roll lately, I am pleased to bring you all more content that he has sent my way:
Although the American Recovery and Reinvestment Act of 2009 (the Recovery Act) provided nearly $28 billion to state governments for improving U.S. highways, the highway system saw no significant improvement. For example, relative to the years before the act, the number of structurally deficient or functionally obsolete bridges was nearly unchanged, the number of workers on highway and bridge construction did not significantly increase, and the annual value of construction put in place for public highways barely budged. The author shows that as states spent Recovery Act highway grants, many simultaneously slashed their own contributions to highway infrastructure, freeing up state dollars for other uses. Next, using a cross-sectional analysis of state highway spending, the author shows that a state’s receipt of Recovery Act highway dollars had no statistically significant causal impact on that state’s total highway spending. Thus, the amount of actual highway infrastructure investment following the act’s passage was likely very similar to that under a no-stimulus counterfactual.
The paper is by Bill Dupor, of the St. Louis Fed. Kevin is a shy, unassuming man, a family man at that, and still deeply underrated!
Delta Air Lines did not sell the middle seat in 2020 during the COVID-19 pandemic.
Its principal rivals sold all seats starting in July 2020.
Delta raised its fares by 15%.
Passengers paid $23 to prevent a stranger from sitting next to them.
Delta had to operate more flights, so this was not a profit-enhancing strategy.
🚨Interim results of @TogetherTrial of ivermectin and fluvoxamine for early treatment of #COVID_19 🚨#ivermectin : no significant effect#fluvoxamine: ↘️ risk of hospitalization by 31%
These important results deserve a🧵 1/n https://t.co/oSy1p91hs2
— Julien Potet (@julienpotet) August 12, 2021
That is from a project funded by Fast Grants.
We reevaluate the 2000s housing cycle from the perspective of 2020. National real house prices grew steadily between 2012 and 2019, with the largest price growth in the same areas that had the largest booms between 1997 and 2006 and busts between 2006 and 2012. As a result, the areas that had the largest booms also had higher long-run price growth over the entire 1997-2019 period. With “2020 hindsight,” the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound.
We argue that this pattern reflects a larger role for fundamentals than previously thought.
As I see it, there was a “negative bubble” circa 2008-2009, based on panic about the shadow banking system that was at the time reasonable but also turned out to be wrong. You can argue however that there was a small bubble at the time (see Figure 1 in the paper, and compare that say to the Japanese stock market), or a bubble in a few particular regions. And do you know who got this right at the time? Our own Alex T., perhaps he will tell you the story in more detail.
The authors continue:
A few papers ascribe a role to fundamental factors in the 2000s cycle as we do. Writing near the peak of the boom, Himmelberg et al. (2005) found “little evidence of a housing bubble” because of fundamental growth, undervaluation in the 1990s, and low interest rates. Ferreira and Gyourko (2018) estimate the timing of the boom across cities and show that the beginning of the boom was “fundamentally based to a significant extent” but that fundamentals revert in roughly three years. We similarly conclude that fundamentals played a significant role in the boom, but based on different methods that focus on long-term fundamentals rather than short-term income growth. More recently, Howard and Liebersohn (2021) propose an explanation for housing cycles based on divergence in regional income growth, in which fluctuations in fundamentals fully explain the cycle, and Schubert (2021) identifies spillovers of fundamentals across cities via migration networks.
You are not going to hear many mea culpas on this one, but a quick look at today’s housing market makes it pretty clear who was right and who was wrong.
Here is the NBER working paper from Gabriel Chodorow-Reich, Adam M. Guren, and Timothy J. McQuade.
“A new research study by one of us and his Johns Hopkins colleagues found that of the $42 billion the National Institutes of Health spent on research last year, less than 2% went to Covid clinical research…
● Of the $42 Billion 2020 NIH annual budget, 5.7% was spent on
● Public health research was underfunded at 0.4% of the 2020 NIH
● Only 1.8% of the 2020 NIH budget was spent on COVID-19 clinical
● Average COVID-19 NIH funding cycle was 5 months
● Aging was funded 2.2 times more than COVID-19 research
● By May 1, 2020, 3 months into the pandemic, the NIH spent 0.05%
annual budget on COVID-19 research
● Of the 1419 grants funded by the NIH:
• NO grants on kids and masks specifically
• 58 studies on social determinants of health
• 57 grants on substance abuse
• 107 grants on developing COVID-19 medications
• 43 of the 107 medication grants repurposed existing drugs
Ouch. Here is a not entirely random sentence from the report:
The COVID-19 pandemic has only exacerbated the NIH institutional challenges and inability to reallocate funds quickly to
Here is another damning sentence, though it damns someone other than the NIH:
…to date, no research has investigated NIH COVID-19 funding patterns to the best of our knowledge.
Double ouch. Might the NIH have too much influence over the allocation of funds to be investigated properly? Rooftops, people…