Category: Data Source

Covid-19 is also not so great for young people

Young adults are dying at historic rates. In research published on Wednesday in the Journal of the American Medical Association, we found that among U.S. adults ages 25 to 44, from March through the end of July, there were almost 12,000 more deaths than were expected based on historical norms.

In fact, July appears to have been the deadliest month among this age group in modern American history. Over the past 20 years, an average of 11,000 young American adults died each July. This year that number swelled to over 16,000.

The trends continued this fall. Based on prior trends, around 154,000 in this demographic had been projected to die in 2020. We surpassed that total in mid-November. Even if death rates suddenly return to normal in December — and we know they have not — we would anticipate well over 170,000 deaths among U.S. adults in this demographic by the end of 2020.

That is from Jeremy Samuel Faust, Harlan M. Krumholz and at the NYT.  To be clear, this is not the main problem, but it is not a nothingburger either.  3,656 deaths per day right now, no matter what the ages how many other American catastrophes can rival that?  #1 cause of death in the country right now, bar none.

From David Splinter, from my email

This is all David:

A related paper by BEA came out today with their updated distributional estimates of personal income. Marina Gindelsky has done a lot of work to produce these estimates.

I have a couple new papers on tax progressivity and redistribution that may be of interest to you. Both used CBO data to avoid the PSZ-AS differences. Abstracts below.

The first paper is about the ends of the distribution: tax progressivity has increased significantly since 1979 (and steadily since 1986) due to more generous tax credits for the bottom, while average tax burdens of the top have been relatively unchanged because lower marginal rates were offset by decreased use of tax shelters. The online appendix shows why the CBO estimates differ from those of Saez and Zucman (see Fig. B7 at the end; it’s mostly due to refundable credits at the bottom and imputed income at the top) and the Heathcote et al. paper you blogged about a couple months ago (it’s technical differences and their inclusion of some transfers, but their most similar measure of tax progressivity was not flat—it increased 21 percent since 1979).

The second paper, with Adam Looney and Jeff Larrimore, is about the middle of the distribution. Since 1979, we found that non-elderly middle-class market income increased 39 percent in real per person terms. The increase was 57 percent when accounting for taxes and transfers. This seems to fit with the “updated” view of stagnation—expanding male wages to also look at untaxed compensation and including female compensation and taxes/transfers shows larger median growth. But there was a structural break in 2000. Before then, middle-class incomes grew at the same rate before and after taxes and transfers, and since then income after taxes and transfers grew three times faster (Fig. 6 on page 19). We don’t discuss the recent market income slowdown (maybe related to the debated labor share break around 2000), but we show that the additional fiscal support that filled the gap looks like an unsustainable way to boost middle-class disposable incomes going forward.”

Are the elites worse than you think?

Here is a new and important paper by Joshua D. Kertzer, noting that it mainly confirms what I observe every day (aren’t those the very best research studies?)  Here is part of the abstract:

…political scientists both overstate the magnitude of elite-public gaps in decision-making, and misunderstand the determinants of elite-public gaps in political attitudes, many of which are due to basic compositional differences rather than to elites’ domain-specific expertise.

My rewrite of his sentence is that elites are arguing from their class and demographic biases (a bias can be positive, to be clear), not from their expertise.  That lowers the marginal value of expertise, at least given how our world operates.  I recall earlier research blogged by Alex showing that if you are a French economist, your views are more influenced by being a French person than by being an economist.  And so on.

This is one of the very most fundamental facts about our world, and elites are among the people least likely to have internalized it.

Have a nice day.

How to build Haitian state capacity

Strengthening state capacity in low income countries requires raising tax revenue while maintaining political stability. The risk of inciting political unrest when attempting to increase taxes may trap governments in a low-tax equilibrium, but public goods provision may improve both tax compliance and political stability. To test these questions empirically, I partner with the national tax authority and a local mayor’s office in Haiti to cross-randomize both tax collection and public goods across one of the country’s largest cities. Effects are measured both via administrative data on tax revenue as well as through novel measures of political unrest. In the paper’s main result, I show that hand-delivering property tax invoices reduces individual tax compliance by 48%, and increases independently observed measures of localized political violence by 192%. In contrast, providing a valuable and visible public good (namely municipal garbage removal) increases tax compliance by 27%, and reduces localized political violence by 85%. Importantly, public goods provision significantly mitigates the adverse effects of tax collection in neighborhoods receiving both treatments. A cost accounting exercise suggests that providing the public good in this setting could pay for itself within the first year. These findings suggest that it may be possible to peacefully shift to a new equilibrium of higher tax compliance with a sufficient initial investment perhaps financed through foreign aid or other transfers.

That is a paper from Benjamin Krause, a job market candidate from UC Berkeley.  Here is his home page and CV.  He was also four years Chief of Staff to Sean Penn, check out the vita.

Youyang Gu will estimate vaccination numbers and herd immunity

  • We believe COVID-19 herd immunity (>60% of population immune) will be reached in the US by late summer/early fall 2021 (Sep-Nov 2021).
  • At the time herd immunity is reached, roughly half of the immunity will be achieved via natural infection, and the other half will be achieved via vaccination.
  • New COVID-19 infections may become negligible before herd immunity is reached. Our current best estimate of when daily community transmissions will drop below 1,000 per day is summer 2021 (Jul-Sep 2021).
  • Summarizing the above findings, our best estimate of a complete “return to normal” in the US is late summer 2021 (Aug-Oct 2021).
  • We estimate around 30% of the US population (~100 million) will have been infected by the SARS-CoV-2 virus by the end of 2021. This translates to a final US COVID-19 death toll of roughly 500,000 (+/-100k) reported deaths.

You can track the data here, his earlier forecasts of cases, hospitalizations, and the like were among the best.  He is here on Twitter.  For the pointer I thank CatintheHat.

Why do wealthy parents have wealthy children?

From Andreas Fagereng, Magne Mogstad, and Marte Rønning

Abstract: We show that family background matters significantly for children’s accumulation of wealth and investor behavior as adults, even when removing the genetic connection between children and the parents raising them. The analysis is made possible by linking Korean-born children who were adopted at infancy by Norwegian parents to a population panel data set with detailed information on wealth and socio-economic characteristics. The mechanism by which these Korean-Norwegian adoptees were assigned to adoptive families is known and effectively random. This mechanism allows us to estimate the causal effects from an adoptee being raised in one type of family versus another.

Here is a (possibly gated) link.

Cognitive biases: where do we stand?

Here is a new and very important paper by Victor Stango and Jonathan Zinman, here are some of the main results, noting that each and every paragraph is important:

Our first finding is that biases are more rule than exception. The median consumer exhibits 10 of 17 potential biases. No one exhibits all 17, but almost everyone exhibits multiple biases; e.g., the 5th percentile is 6.

Our second finding is that cross-consumer heterogeneity in biases is substantial. The standard deviation of the number of biases exhibited is about 20% of its mean, and several results below suggest that this variance is economically meaningful and not substantially inflated by measurement error.

Our third finding is that cross-consumer heterogeneity in biases is poorly explained by even a “kitchen sink” of other consumer characteristics, including classical decision inputs, demographics, and measures of survey effort. Most strikingly, we find more bias variance within classical sub-groups widely thought to proxy for behavioral biases than across them. E.g., we find more bias variation with the highest-education group than across the highest- and lowest-education groups.

Our fourth finding is that our 17 biases are positively correlated with each other within-consumer, especially after accounting for measurement error following Gillen et al. (2019).1Across all biases, the average pairwise correlation is 0.13, and 18% have p-values < 0.001. Within six theoretically-related groups of biases (present-biased discounting, inconsistent and/or dominated choices, risk biases, overconfidence, math biases, and limited attention/memory), the average pairwise correlation is 0.25 and 29% have p < 0.001.

Our fifth finding is that there are also some important correlations between biases and classical inputs. Classical inputs and demographics may not explain much of the variance in biases (per finding #3), but some of them are correlated with biases in patterns that align with prior work. Most notably, the average pairwise correlation between cognitive skills and biases is -0.25. Cognitive skills are strongly negatively correlated with most biases, but positively correlated with loss aversion and ambiguity aversion. Other classical inputs are relatively weakly correlated with biases, except for a few expected links between patience and present bias, risk aversion and aversion to uncertainty and losses, and risk aversion and math biases that can lead to undervaluation of returns to risk-taking.

Overall not encouraging!  But perhaps some of that is also what makes life more meaningful, at a high cost admittedly.

Who Mismanages Student Loans and Why?

From Kimberly Rodgers Cornaggia and Han Xia:

With a license to use individually identifiable information on student loan borrowers, we find that a majority of distressed student borrowers manage their debt sub-optimally and that suboptimal debt management is associated with higher loan delinquency. Cross-sectional analysis indicates that loan (mis)management varies significantly across student gender, ethnicity, and age. We test several potential selection-based explanations for such demographic variation in student loan management, including variation in students’ overconfidence, consumption preferences and discount rates, and aversion to administrative paperwork. Motivated by federal and state allegations against student loan servicers, we also test for the presence of treatment effects. Overall, the empirical evidence supports the conclusion that loan servicers’ differential treatment across borrowers play an important role in student loan outcomes.

Here is a key background fact:

Broadly, subsidized student borrower assistance programs include provisions for loan forbearance, loan deferment, and
income-driven repayment (IDR) options for financially distressed borrowers.

Borrowers should switch to those provisions more than they do, with older students, non-traditional students, males, and non-whites performing less well than others.  Here is the link to the paper, via the excellent Kevin Lewis.

How good has media coverage of Covid-19 been?

We analyze the tone of COVID-19 related English-language news articles written since January 1, 2020. Ninety one percent of stories by U.S. major media outlets are negative in tone versus fifty four percent for non-U.S. major sources and sixty five percent for scientific journals. The negativity of the U.S. major media is notable even in areas with positive scientific developments including school re-openings and vaccine trials. Media negativity is unresponsive to changing trends in new COVID-19 cases or the political leanings of the audience. U.S. major media readers strongly prefer negative stories about COVID-19, and negative stories in general. Stories of increasing COVID-19 cases outnumber stories of decreasing cases by a factor of 5.5 even during periods when new cases are declining. Among U.S. major media outlets, stories discussing President Donald Trump and hydroxychloroquine are more numerous than all stories combined that cover companies and individual researchers working on COVID-19 vaccines.

Emphasis added by me.  That is the abstract of a new NBER working paper by Bruce Sacerdote, Ranjan Sehgal, and Molly Cook.

Do pandemics boost public faith in science?

No, according to Barry Eichengreen, Cevat Giray Aksoy, and Orkun Saka:

It is sometimes said that an effect of the COVID-19 pandemic will be heightened appreciation of the importance of scientific research and expertise. We test this hypothesis by examining how exposure to previous epidemics affected trust in science and scientists. Building on the “impressionable years hypothesis” that attitudes are durably formed during the ages 18 to 25, we focus on individuals exposed to epidemics in their country of residence at this particular stage of the life course. Combining data from a 2018 Wellcome Trust survey of more than 75,000 individuals in 138 countries with data on global epidemics since 1970, we show that such exposure has no impact on views of science as an endeavor but that it significantly reduces trust in scientists and in the benefits of their work. We also illustrate that the decline in trust is driven by the individuals with little previous training in science subjects. Finally, our evidence suggests that epidemic-induced distrust translates into lower compliance with health-related policies in the form of negative views towards vaccines and lower rates of child vaccination.

Here is the link to the NBER working paper.

The pandemic is indeed a big deal

In our estimation, and with standard preference parameters, the value of the ability to end the pandemic is worth 5-15% of total wealth. This value rises substantially when there is uncertainty about the frequency and duration of pandemics. Agents place almost as much value on the ability to resolve the uncertainty as they do on the value of the cure itself.

That is from a new NBER working paper by Viral V. Acharya, Timothy Johnson, Suresh Sundaresan, and Steven Zheng.  Their analysis also shows that preventing or limiting future pandemics may be a bigger deal yet.