Mask research is in the news again with headlines like "mask mandates don't work".
I'll write my views here on how we should think about mask-wearing based on the research summarized in #CochraneReview
[Only minimum statistical literacy required to follow the argument]
Different studies are designed to answer 3 related but *different* questions 1. Do masks work when people actually wear them?
vs 2. Do mask mandates work?
vs 3. Does softly encouraging mask-wearing work?
We have good evidence that #1 works (RCTs in hospitals, lab studies,...]
To study the effect of mask-wearing in the real world, you have to first get people to wear masks.
The treatment can be a mandate or some encouragement.
That experiment can only answer 2. What is the effect of imposing a mandate?
or 3. What is the effect of encouragement?
NOT #1
If the mandate or encouragement does not change actual mask-wearing sufficiently in a first stage,
then
you won't be able to tell statistically whether mask-wearing reduces transmission in a second stage.
2nd stage study is under-powered because the first stage was weak
This is why half our #BangladeshMaskStudy seriously engaged with the Social Science (not Epi) question:
"How do you get people to wear masks?"
Only with sufficiently large increases in mask-wearing are we powered to answer the effectiveness question. science.org/doi/10.1126/sc…
With due respect to #CochraneReview colleagues, if you don't clarify these social science and epi nuances, we risk confusing or misleading journalists and the public, which is happening here.
So where does this leave the public and policymakers in terms of their decisions?
If you or others around you are sick, wear masks!
Mandating mask-wearing only useful if you can enforce the mandate.
Soft encouragement doesn't do much.
People's reluctance to wear indicates that there are costs to mask-wearing. This means we should not mandate when risk is low
I said due respect to @cochranecollab, but one of the authors is claiming that our methods are opaque when literally ALL our data and code are publicly posted.
If the authors don't understand standard statistical methods, that doesn't make it "opaque"
My twitter feed is full of Ph.D. admissions decisions. Let me instead share with applicants and letter-writers what I learned after reading ~250 files this year, and then spending 18 hours in meetings with my colleagues on the Ph.D. admissions committee discussing those files.
Ph.D. admissions decisions are very important, and we take them extremely seriously at @YaleEconomics. The 18 hours of meetings *after* files read by multiple faculty easily exceed the meeting time allocated to either junior or senior faculty recruiting!
Despite that, we will certainly make mistakes. There are 150-200 applicants who check most boxes and would likely succeed, but our class size is ~20. I would NOT bet my house on
{Applicant we ranked 33 will prove to be more successful as an economist than Applicant we ranked 66}
Some misinterpretation of our #Bangladesh Mask RCT by those who don’t read research, which I’ll ignore, but also some parochial/racist reactions, which I must respond to nbcnews.com/science/scienc…
1."Americans experimenting on Bangladeshis to inform Americans”. Sorry, not all work is done to service Westerners. I [co-lead (clearly signaled as last) author], am Bangladeshi. We conducted study to primarily benefit LMICs. Study sites included districts where my family resides
We did NOT withhold masks from control group for research. Even while data collection was being completed, we personally secured donations of 100 million+ masks to distribute for free in 🇧🇩🇮🇳🇵🇰🇳🇵because the research showed positive effects.
Educated, skilled workers are more likely to emigrate away from polluted cities. This affects aggregate productivity and welfare, and also explains ~14% of an enduring macro-development puzzle:
Why do people remain in low-productivity areas when big cities offer higher wages?
Greater out-migration of the college-educated from polluted areas is clearly evident even in the raw data in China (see maps), but we use multiple data sources and empirical techniques to uncover the causal effect of pollution on emigration
Implication for aggregative productivity and pollution policy?
Emigration response of the skilled means that the unskilled left behind become less productive. Skilled and unskilled workers are complements in production in China. Asymmetric migration creates a spatial mismatch
A thread on my sense of the immediate #policypriorities for @JoeBiden & @KamalaHarris administration, to undo the most consequential damages wrought by the last 4 years. A journalist’s question forced me to think about #economicpolicy, so I thought I'd get your reactions. [1/9]
First, bring #COVID under control. There’s no lives vs livelihoods tradeoff. Economy will move only when the virus is contained. Rich people need to feel comfortable to go out, to spend money. They hold back due to fear of contracting COVID, not due to any #lockdown [2/9]
The strategies are simple: Lead by example to instill a sense of civic duty. Inspire citizens to wear masks and make small sacrifices to protect each other. Put the amazing US #publichealth talent in charge to develop robust testing, tracing [3/9] cnn.com/2020/11/03/afr…
#COVID spreads through human-to-human transmission, so #migrants are an important vector. In the absence of adequate covid tests in LMICs, can we predict sub-national COVID spread, or identify likely hotspots using data on migration?
Data on airport returnees predict subsequent quarantines & #COVID19 distress calls across districts in #Bangladesh. Data on migration permits predict confirmed cases in #Philippines municipalities and Bangladeshi sub-districts.
Beyond the validation using public health data, our recent phone surveys across Bangladesh helps to ground-truth this approach:
Living in communities with recent #migrant returnees triples the odds (!) of reporting #COVID symptoms. This is the single largest risk factor.
Nearly a million #Rohingya refugees reside in densely packed camps in Coxbazar, Bangladesh. We conducted surveys of representative samples of refugees and Bangladeshi hosts living near camps after #COVID19 crisis hit. Alarming findings out in @WHOBulletin: who.int/bulletin/onlin…
Both hosts and refugees face significant economic distress.
59% of hosts and 72% of refugees unable to buy essential items.
Sharp decrease in employment: 76% of males in host communities were employed in July 2019, but only 21% today.
Coauthors: Paula Lopez-Pena @caustindavis
25% of Rohingyas and 13% of hosts report at least one COVID symptom (defined as per WHO guidelines).
Many are high-risk w/ underlying disease.
Economic vulnerability doubles the odds of COVID symptoms.
Living in communities with recent migrant returnees triples the odds.