Paul Goldsmith-Pinkham Profile picture
Oct 3, 2022 17 tweets 7 min read Read on X
New NBER working paper with @jwswallace and @jasonlschwartz on Covid mortality: “Excess death rates for Republicans and Democrats during the COVID-19 pandemic” nber.org/papers/w30512

Ungated on arxiv here: arxiv.org/abs/2209.10751

Thread 🧵1/ Political affiliation has emerged as a potential risk factor
2/ A popular commentary on the Covid crisis has been how much higher the Covid death toll has been in Republican vs. Democratic counties in the U.S.

nytimes.com/2021/11/08/bri…
3/ There are natural reasons to believe that there are strong differences by Republicans vs. Democrats: survey data suggests that there are big differences by party ID on Covid-19 vaccination: kff.org/coronavirus-co…
4/ The challenge, of course, is whether it’s really about Republicans vs. Democrats living in these areas, or just the areas where individuals sort into are different.
5/ This statistical analysis runs into a serious challenge, driven by the fact that publicly available data on Covid deaths, and measures of political party, are typically only available at the county level.
6/ The focus on Covid deaths and counties has lead researchers to try to account for these locational differences (by controlling for features at the county level), but are still limited by the aggregated nature of the data:
healthaffairs.org/doi/full/10.13…
7/ The other issue with this approach is that it focuses on reported Covid deaths as an aggregate measure. This measure may not fully capture the “counterfactual” deaths in the absence of the pandemic. Our world in data does an excellent discussion: ourworldindata.org/excess-mortali…
8/ Intuitively, calculating excess death rates requires a prediction of death rates in 2020 and 2021 based on previous years for the group of interest: namely Democrats and Republicans. Fortunately, we have mortality data with party affiliation, age, and location in this paper!
9/ We construct data using *individual-level* voter registration in 2017, linked to death records from 2018 to 2021, for Ohio and Florida. We then construct excess death rates that control for differences in mortality rates (pre-Covid) at the age-by-party-by-county-by-month level
10/
This lets us ask and answer three questions:
11/
Q1: Does excess death in 2020 and 2021 differ by political party, how much and when does this occur?

A1: Yes, the excess death rate for Republicans was 5.4 p.p., or 76%, higher than for Democrats. The gap was exclusively in the post-vaccine period (10.4 pp or 153%).
12/
Q2:
Is this difference explained by geographic or age differences in political party affiliation?

A tiny share of the difference is explained by differential impacts of age-by-county *during Covid* (recall that excess deaths already controls for pre-Covid differences):
13/

Q3: How much can we point to vaccines?
A3: This is harder, since we don't have individual-level data on vaccines. However, two facts emerge:

A. The association between the Rep.-Dem. gap and county-level vaccination rates grows significantly after they become available:
14/

B. Moreover, *pre-vaccine*, the relationship across counties between realized vax rates and excess deaths was identical for both groups.

Post-vaccine, the Democrat rate fell and Republican rate climbed; and the gap between the two was near zero in high-vax counties.
16/ If this is really a story about vaccines, the continued story of low take-up of vaccines + boosters among Republicans may perpetuate some of these differences: kff.org/coronavirus-co…
17/ We’re working on expanding this out now to contrast our results with the existing literature a bit and highlight some more points, but would welcome any comments or suggestions.
fin/ It is important to reiterate that our results hold fixed differences in mortality by age, location, and party pre-Covid, and can account for location-by-age differences post-Covid. Hence these are within-age-and-location differences in mortality outcomes by political party.

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Paul Goldsmith-Pinkham

Paul Goldsmith-Pinkham Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @paulgp

Mar 8
Finally posting a new paper on how diffusion estimates on networks (e.g. for epidemics, information spread, tech adoption) can be highly non-robust to even tiny (vanishing!) measurement errors. 🧵

[Link to paper: ]
1/ paulgp.github.io/papers/diffusi…
Image
What is a diffusion model? It's a way to study how things spread through a network. For example, how a disease spreads through a population, or how information spreads through a social network.

These models are used to do research and make policy decisions!

2/

Image
Image
Image
In practice, when we operationalize these models, we estimate the network relative to the true network.

Concerns about measurement error in networks are not new, but it turns out that with diffusions, measurement error are especially bad.

3/
Read 16 tweets
Aug 15, 2023
2023 was a crazy year -- remember how we had a bank run at Silicon Valley Bank that caused a banking sector collapse (over 20% decline in bank equities in a week!) and prompted a Federal Reserve facility intervention?

No? Well then I have the thread and working paper for you! 🧵 Image
Following the collapse of SVB, there an immediate response in the stock market. Many banks other than SVB plunged (most notably First Republic Bank, who finally failed many weeks later). Overall, the bank sector corrected sharply downward. Image
There was, however, significant heterogeneity in this downward correction. In the first week, the decline was quite skewed, with smaller banks experiencing less of a decline. By May, more banks had followed, leading to a dispersed, symmetric (and negative distribution).
Image
Image
Read 19 tweets
Mar 21, 2023
Bank failures are a common phenomenon in the United States.

In the 30s and 40s, the FDIC had 20 bank failures a year.

In the 50s and 60s, a lull of 3 per year.

In the 70s, this picked up to 9 per year.

In the 1980s and 90s, an average of 150 banks failed per year.

🧵
What is distinctive is how much the size of the banks failing has changed, even when adjusting for inflation. The total assets is swamped by the size of the crisis in 2008, but even just looking at the average size banks that fail, it is striking:
It's not even possible to discern much of the bank failures in the 1930s and 40s on this scale when compared to the 2008 crisis, but we *can* see them if we use a log scale.
Read 6 tweets
Mar 11, 2023
The market definitely thinks there are more banks that will be run on.

Here's the 7 banks with the largest decline in the market in the last two weeks.

1/🧵
We have:

SVB Financial Group (SIVB) (-60%)
PacWest Bancorp (PACW) (-54%)
Signature Bank (SBNY) (-36%)
Western Alliance Bancorp (WAL) (-32.4%)
First Republic Bank (FRC) (-31.3%)
Customers Bancorp Inc (CUBI) (-23.5%)
First Foundation Inc (FFWM) (-20.3%)

2/
But they're not alone! Several banks experiencing 15-20% declines:

3/
Read 10 tweets
Nov 3, 2022
This is a really interesting blog post about DSGE models -- almost makes me want to get into the identification / estimation issues around macro structural models! (almost)

From the paper:
From the blog:
This feels like absolute catnip for @dan_p_simpson and all the Bayesian estimation folks
Read 4 tweets
Sep 30, 2022
How it started how it's going ImageImage
Two children and a pandemic later
How it really started Image
Read 5 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(