Phil Magness is a Senior Research Fellow at the American Institute for Economic Research.<\/p>\n\n\n\n
He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.<\/p>\n","protected":false},"excerpt":{"rendered":"Phil Magness is a Senior Research Fellow at the American Institute for Economic Research. He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.","protected":false},"author":13,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","episode_type":"audio","audio_file":"http:\/\/podcasts.la.utexas.edu\/cepa\/wp-content\/uploads\/sites\/21\/2020\/06\/2020-06-23_Policy-at-McCombs_Phil-Magness.mp3","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"67.13M","filesize_raw":"70393088","date_recorded":"25-06-2020","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":""},"tags":[96,30,95],"categories":[],"series":[2],"class_list":["post-549","podcast","type-podcast","status-publish","tag-economic-inequality","tag-economics","tag-taxation","series-policymccombs","entry"],"acf":{"related_episodes":"","hosts":[{"ID":693,"post_author":"38","post_date":"2020-10-29 17:58:44","post_date_gmt":"2020-10-29 17:58:44","post_content":"\n
Carlos M. Carvalho is an associate professor of statistics at McCombs. Dr. Carvalho received his Ph.D. in Statistics from Duke University in 2006. His research focuses on Bayesian statistics in complex, high-dimensional problems with applications ranging from finance to genetics. Some of his current projects include work on large-scale factor models, graphical models, Bayesian model selection, particle filtering and stochastic volatility models.<\/p>\n\n\n\n
Before moving to Texas Dr. Carvalho was part of the faculty at The University of Chicago Booth School of Business and, in 2009, he was awarded The Donald D. Harrington Fellowship by The University of Texas, Austin.<\/p>\n\n\n\n
Dr. Carvalho is from Rio de Janeiro, Brazil and before coming to the U.S. he received his Bachelor's degree in Economics from IBMEC Business School (Rio de Janeiro) followed by a Masters's degree in Statistics from the Federal University of Rio de Janeiro (UFRJ).<\/p>\n","post_title":"Carlos Carvalho","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"carlos-carvalho","to_ping":"","pinged":"","post_modified":"2020-10-29 17:59:59","post_modified_gmt":"2020-10-29 17:59:59","post_content_filtered":"","post_parent":0,"guid":"http:\/\/podcasts.la.utexas.edu\/cepa\/?post_type=speaker&p=693","menu_order":0,"post_type":"speaker","post_mime_type":"","comment_count":"0","filter":"raw"}],"guests":[{"ID":624,"post_author":"42","post_date":"2020-07-03 20:21:08","post_date_gmt":"2020-07-03 20:21:08","post_content":"\n
Phil Magness is a Senior Research Fellow at the American Institute for Economic Research.<\/p>\n\n\n\n
He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.<\/p>\n","post_title":"Phillip Magness","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"phillip-magness","to_ping":"","pinged":"","post_modified":"2020-07-03 20:28:44","post_modified_gmt":"2020-07-03 20:28:44","post_content_filtered":"","post_parent":0,"guid":"http:\/\/podcasts.la.utexas.edu\/cepa\/?post_type=speaker&p=624","menu_order":0,"post_type":"speaker","post_mime_type":"","comment_count":"0","filter":"raw"}],"transcript":"
Welcome to Policy McComas. A data focused conversation on tradeoffs.
\n\ue5d4
\nI’m Carlos Kavala from the Saban Center for Policy at the University of Texas at Austin.
\nWe have with us today Philip Magnus, senior research fellow at the American Institute for Economics Research.
\nHe’s the author of numerous works in economic history, taxation and Equality in Education Policy and been right and
\nhas been writing extensively about global health. Thanks for joining us. Thanks for having me.
\nSo let’s let’s go back to March. One of the things we’ve been trying to do is to go back to what we thought when
\nand how the with information we had at that point. So, you know, even if it was before March, when
\nwere you started to be like, huh, this is happening and what are we gonna do about
\nit? What was your sort of wake in any moment on it? Yeah. See, the first sign that I noticed
\nanything about it really coming into kind of a serious direction very
\ninto February, The New York Times really started ramping up its coverage
\nof Koepp it and I noticed it at the time. I wasn’t expecting, you know, anything
\nnear like what we had at the moment or the lockdowns or anything that followed. But I noticed that
\nsuddenly there their attention had shifted pretty intensely to that subject. And then some
\nof that was actually sounding kind of a fairly alarmist direction. So this is probably around February
\nmoment when I knew this was serious, when I knew that we should kind of buckle in for
\na very long policy response was actually I think it was on March 12th
\nand that was the day not the government did anything was the day that the NBA canceled
\nthe remainder of the season. Yeah, it’s it’s incredible that,
\nagain, by individuals and reacted to sometimes faster and start internalizing,
\nthere’s a little bit quicker than a government, right? Exactly. Governments, the lagging indicator here.
\nRight. Right. So so the NBA does it. And what do you start looking at in terms of data or models or
\nwhat? Catch your attention in terms of the information available for that decision. So
\nwe saw on March 16th was the big day that major decisions were made both in the United States
\nand the UK. And that was the day that the Imperial College Niall Ferguson model came out.
\nAnd, you know, everyone remembers that. That’s the one that predicted two point two million deaths in the US. Five
\nhundred thousand in the UK. And it’ll be over by last year. You remember
\nthat. But I actually I can remember now from a top my head by when they predicted 2.2 million deaths in the US. So
\nit was it was a little bit unclear. And the model they were claiming,
\none version of it stated by the end of the year, but that he also gave some public comments to reporters
\nthat it ended indicated slightly different timeframes. But I think the assumption has generally
\nbeen by. By the end of 2020. OK. OK. And and yes, this is that
\nwas the model that somehow changed course on the UK response rate. In case
\nyou know me on the path that Sweden had decided to follow and that
\nmodel came in and change might not only change what the UK did, but I think it was very influential in our
\ndecision across the states here. Right, right. Right. Yeah. Here we have quotes from Anthony
\nAlgy and Deborah Burke saying that they they had just received the new model from the UK
\nand it was predicting this disastrous scenario here. So it ends up being a major thing that shifts
\nthem as well as the UK government, the Imperial College, sexually advising. So it’s something that at
\nface value, I think the model prediction as perhaps assumed that that was the best science
\nwe had at the time. How would you take that information and think
\nabout what to do and think about the options on the table? Right. And I think the main options that table at that point in time,
\nwe’re like, well, we can try to do this the way Sweden is doing or we can try to do this the way
\nnotor literally had done or what we decided experiment and locking down. So what should we
\nbe looking at? Even taking that assface started it at point 2.2 billion. That’s prediction. How do
\nwe go about making that decision? Well, my my first reaction to this and this is
\nprobably within the days after the Imperial College model came out, is we had to look at the track
\nrecord of this group. This group of scientists, because they used an older model that was
\nfrom the influenza epidemics in the in the mid 2000s when they
\nadapted it over to CO. Did they state this outright? They said we’re taking our old flu model because this is the
\nclosest thing that they thought they had decoded. So my next question is let’s look how that flu model
\nperformed in 2005 and then 2009 when these were the avian
\nand swine flu years that we did have a serious response to.
\nBut we can also evaluate against the way that the model performed in action.
\nAnd it turns out if you go dig up the articles, they were predicting hundreds of thousands of deaths with each of these pandemics,
\nand it ends up being a tiny fraction of that. So that immediately tells me
\nthat maybe we shouldn’t put as much stock in this specific model that
\ngovernments actually did. And then the second question that we need to ask is, you know, these models
\nare based on inputs in back in March, even till very recently, we didn’t
\nhave much in the way of reliable data on the nature of this new disease.
\nWe knew that it was deadly. We knew that it spread very clearly in nursing homes because, you know, in Washington state,
\nthe first outbreak in the US was in nursing homes. We knew out of Italy that
\nelderly populations were especially vulnerable and that had been where the majority of the deaths had occurred
\nby far and Italy. But we didn’t know the infection fatality rate. We still don’t
\nhave a clear piece of evidence about that. And then testing was also so bad.
\nI mean, it was just kind of like a crapshoot. You didn’t really know what was going on. And yet
\nall these things are necessary to kind of calibrate the parameters of the type of modeling
\nthat they employed there. And that raises a huge red flag for me. That that’s
\nthat’s kind of science. Flying blind into the wind and
\nnot really knowing what’s going to going to happen. And yet pretending as if you have almost like this pretense
\nof knowledge that you can project not only weeks out, but years out,
\nthe epidemiological pattern of this disease and all using a model that hasn’t performed
\nall that well in the past. Right. But so one would try to justify
\nwhat we did by looking at that and say, OK, we don’t know enough. There’s lots of unknowns here. We don’t have enough testing.
\nWe don’t have enough information. But under a combination of parameters and a combination of assumptions,
\nwe’re facing 2.2 million deaths. Right. So but there’s something else that was missing
\nin that discussion when when deciding, let’s say to you, and then that’s the tradeoff
\ncalculation. So, again, we can sit here and let this ride and,
\nyou know, we’re going to kill 2.2 million people and or we can can do something that is extremely
\nintrusive, extremely right. Just like unpredictable in its nature. And what was gonna do
\nand what’s going to generate. So. So as as the lock announced our role again, what
\nwere the things that you would think immediately? Obvious destruction. OK, well, the job market is going to suffer and I’m going to go.
\nBut how were you thinking about that side, that the tradeoffs that we’re starting to face, the immediate
\ntradeoffs that come to mind is we know from past economic recessions and depressions,
\nthere’s an extensive literature on this that it actually has clear effects in
\nboth increases in substance abuse, increases in depression, which leads to suicide,
\nall sorts of ill health effects associated with economic downturns. We know this from 2008
\nwhen when the last major recession happened. Suicides, speed spiked,
\nalcoholism went up, substance abuse went up. And this is a just a natural
\nresponse that people have when when there are massive job losses and poor prospects for the future.
\nSo we can reliably predict that something like that is going to happen. And guess what? It has
\nstarted to play out that way. We know from some states that we’re not hot of coded. They were
\neven reporting suicide rates that are are matching or exceeding what
\nthey’re localized, coded right. Happened to be. So that kind of a downturn
\nis immediately evident, as is something that has to be as part of the tradeoff. Then you start
\nlooking at secondary and and third order effects that are taking place
\nin response to this policy. So we lock down society in response to code.
\nBut that also included the cancelation of what were called elective procedures in many hospitals.
\nIt shut down the remainder of the medical industry so it could Rio oriented focus toward
\nCoatbridge beds. We wouldn’t lose our hospital capacity for a Kogut mitigation. But
\nwhat does that mean? It could be anything from someone who is at an early stage of cancer
\nnow has to delay treatment for several months, and that could have
\nserious severe effects on their their expected lifespan. Not maybe this year, but many years from
\nnow could be anything from elective surgeries that pertain very closely
\nto the quality of life. So elective surgery is not. It’s not like getting a plastic
\nsurgeon to fix your nose or something. It could be a hip replacement. It could be a
\njoint repair. It could be something that treats a medical problem that’s not severe now.
\nBut when you get into old age, it’s going to very, very clearly rack up and cause you problems
\nif you don’t treat it now. And what that implies is that some of the toll of
\nthis is going to not be fully felt until 10, 20, 30 years from
\nnow. For many people, they’re going to find diminished quality of life and maybe
\neven lifespan itself because of treatments that were delayed in response to the lockdowns.
\nSo you start thinking about all these different conditions that come about, and it’s
\nsuddenly a very severe aggregating problem that’s not at all considered in the models. It’s
\nnot this isn’t part of the calculations that someone like Imperial College is
\nmaking. This is the cost side of a decision that’s almost
\noccurring in a vacuum. And so you’re adding those things up as
\ndifficult, of course. But but at the same time, I think we’re true to the two sides in very different
\nways. Right. One side, B, have a lot of uncertainty. Let’s stick to point to a space value and get
\na side less than even talk about it. Let’s not even debate that. Let’s let’s try to
\nadd up the things that we’re going to do. And one of the things that you write a lot from
\nfrom I want to say like economic freedom point of view, I’m not at these. You’re right. And
\nthe one thing that bothered me and I want to take your take on it is that the notion that
\ngovernments start coming in and say, we’ve got to decide what’s essential. It’s not essential. Right. And that’s sort
\nof our inability to understand how, you know, the economic age of context and how things are connected
\nto to define what’s essential nonessential. It’s scary. And,
\nyou know, the implications of that is it’s just incredible and surprises me, actually, that we didn’t suffer
\nmore of a disruption. But then when you start
\ndefining things as essential versus nonessential, that’s a political decision. That is
\nthe governor or the mayor of some state or city coming in and saying, we know that
\nthis type of business is necessary, but these other types of business are not. And we found this in the United
\nStates, some things that were legal in Ohio where illegal just across
\nthe border in Michigan. So the Michigan government governor deemed a gardening supplies
\nnon non-essential. So they had to rope off that section of the store, whereas the crop just across
\nthe border, you could drive five minutes up the road. If you live near the border, you could be in Ohio. And suddenly
\nit’s a very different situation because they made it on political lines. We saw this internationally
\nas well. There was a kind of a quirky story of a
\nstore that sits on the border between the Netherlands and Belgium. And the half of the store that
\nwas on the Belgium side was all roped off to only essential items, whereas the half of the store
\non the on the Netherlands side was basically open, you know, with social
\ndistancing and all the usual precautions in place. But you could sell things in one half
\nof the store and not the other half the store. And you will get this on its face. It’s just kind of absurd.
\nSo I started looking a bit into some of the history of where do we get this definition of essential versus nonessential
\nbecause it is putting it in the hands of the politicians rather than any scientific basis. And
\nif we go back to previous pandemics where they’ve adopted policies to
\ntry to curtail business hours. So the famous one being the Spanish flu in 1917,
\nthere was not an essential versus nonessential distinction that was made. The measure that they used
\nthere was and we found this in city by city by city, they keep going back to it,
\nwhether a store or facility or restaurant or public place was congested.
\nSo they were they were concerned about the number of people, not what the the service or product
\nbeing sold happened to be, which I think even though that’s not a perfect response,
\nit’s a better basis to actually go by. You want to focus on the transmissibility of the disease,
\nnot whether people are choosing to buy gardening supplies or food or toilet paper or whatever it happens
\nto be. Right. Like shutting down the dog groomer seems to be like a really bad idea. Right.
\nVersus interacting on one dog. That’s not that’s not super clever. Yeah, we we we tried
\nto spend some time thinking here about about measuring the stability of different activities and trying to
\nlook at a tradeoff between that activity, employment and GDP and provide that information to some
\nlocal governments here. But again, there was no appetite for that. There was this sort of like dichotomy either.
\nOK, let’s either open or or or close and that they use of these emergent
\nemergency powers to say shelter in place, stay home somehow was was seen as the
\nthe. I want to say the what’s the word in the word you that sort of empathetic,
\nthe right thing to do for for every individual. You wrote a little bit about this as well on like this notion
\nthat we chose a course of action that some people think that Reverdy now is
\nis hurting the most vulnerable. Right. Right. You see, the lockdown is going to hurt the most vulnerable. Our our candidate
\nfor Senate here in Texas a while. I’ll go back to Iraq. Just Texas yesterday tweeted yesterday about how Texas
\nis really hurting the vulnerable by opening the economy. Is it the reverse of that? Right. Here
\nis the exact opposite of that. You get two effects here. You know, when they impose
\nthe lockdowns, what types of businesses are hurt the most? It’s the small businesses.
\nIt’s the local one shop operations. It’s the one shop, cafe or restaurant. And a lot
\nof these are owned by you know, they’re owned by just a single person in
\nthe community. They’re not a big chain that’s nationwide. Meanwhile, Wal-Mart and Target
\nand all these these national chains that do fall under the essential goods exemptions,
\nthey provide groceries. And I’m not saying that they should be shut down any quite the opposite of that. But but
\nthey’re going to be fine through this. And in fact, the business is going to probably shift toward them just like it
\nshifted toward Amazon online. But the businesses that are going to hurt the most are those
\nthat do not have a national political presence or even a statewide political presence
\nto kind of malet a an opposition to this
\npolitically induced presence to try and put them under lockdown. Then the second thing of that,
\nwhenever you enact a lockdown, you are inviting enforcement, you’re inviting
\nagents of the state. And this is police, it’s bureaucrats, it’s regulators, whatever it happens to be,
\nyou’re giving them a power to come in and say anyone that violated this lockdown
\nis now subject to either fine or arrest or imprisonment, imprisonment, all the usual things we do
\nwhen we make something illegal. You have to expect that it’s going to be enforced as it usually
\nis by the government. They’re going to send the police and regulators out. Well, what do we know about enforcement
\nin general, even separate apart from Cobbett patterns in police
\nenforcement patterns and regulatory enforcement tend to overwhelmingly
\nbe focused upon those that are least able to defend themselves. People that don’t have access to
\nhigh dollar attorneys don’t have corporate lobbyists. In other words, the most vulnerable
\nmembers of society. Often that means poor people and racial and ethnic minorities.
\nAnd we know from several weeks of coded enforcement. And we
\nstarted to get some data out of New York City on this in particular. They started to release the numbers
\nof how many people they had arrested for violating the lockdown. And then they did the racial
\nbreakdown. It turned out over 80 percent. Reader, African-American or Hispanic.
\nAnd this is a very large, diverse city, but they all happen to be all the arrests happened to be
\nin. And just basically to racial and ethnic groups. Something is not right. There
\nare some things a very discriminatory there in the way that this is being enforced. It’s
\nalmost as if they’re going into the poorer neighborhoods and then the minority neighborhoods looking for
\nlock down violators. Meanwhile, in the in the wealthy neighborhood in downtown Manhattan, it’s
\nkind of looking the other way. And this is it’s almost a public choice, if that is, you
\nknow, that who’s going to be able to hire an attorney who’s going to be able to
\ncall attention to abusive enforcement and then who isn’t? And at the same time, at the same
\ntime, the most vulnerable, the four have more of an incentive to go out and
\nand not obey the lockdown because. Exactly. Because the market demands a benefit of working
\na day is much higher for them right then and for professor sitting at home, still working like me, where
\nyou know, life. So so that the word I like that. Was it some
\na lot of epidemiologists and some health officials using the word, oh, does any convenience. Right.
\nRight. It might be an inconvenience for me, maybe for you. But for a lot of people, it’s not an inconvenience. It was like the
\nstripe of their livelihoods. Right. And so that the sentence of that word is
\njust unbelievable. Yeah. Yeah. And we saw we saw that all over is the blue chip Twitter mafia.
\nYou want to call on that? Most of these are people that are academics or they’re reporters.
\nThey’re they’re middle class, upper middle class people who are able to work for home.
\nThey can work from home. But if you worked in the service industry, if you worked in retail
\njobs that are more commonly associated with, you know, minimum wage jobs
\nand this is your only source of livelihood or income, and you can no longer go to work because your business is
\nshut down. You can’t sit there and watch Netflix all day and continue to collect your salary
\nand do asume meeting to teach your class. Your livelihood is cut off. All right.
\nSo. So we did it right. So we did all the mistakes of of
\nof not really considering lots of different tournaments. One of the things that I think was very, very
\nnot fair to the public is the fact that the goalposts shifted as well. Why we had very much we had.
\nRight. We had this idea that maybe you’re going to do this for like two weeks to flatten the curve. That was the
\nterm that went viral with the idea of letting the curve. There’s not an idea of changing the total number of infections,
\njust like smoothing the pass, slowing the rate of infection so that we don’t overall
\ncapacity. And maybe I think the target in the beginning, people probably hoped that was like, oh, it’s going to be a two week thing,
\nwhich I hope not at like a deal if it’s like extended holiday two weeks.
\nRight. But what with two weeks became three months. And they go post
\nstarted thinking, well, now we just know about that. And the curve is about somehow eradicating the disease.
\nRight. What was dynamic at you? Did you saw that led to this sort of shifting and the goals of the politicians
\nhere? Yeah. It’s almost like they sold the bill of goods to start off with it because it was all about
\nflattening the curve. That was the meme circulating around the Internet. It was the talking point
\nof Anthony Algy and all these public health officials. We’re gonna do this for a couple of weeks
\nbecause we need to flatten the curve to preserve our hospitals. And just just as you said, that that passes by,
\nthat deadline passes. And so what we need to extend it from April 1st,
\nApril 15th, April 15th, the March to May 15th, May 15th to June. And so on.
\nSo it’s been just kicking the goalpost even further down the
\nline. And even to the point that I was reading an article in The New York Times yesterday
\nwhere they were trying to compare the European response, the Western European response to the American
\nresponse. And now all of a sudden, the talking point is that Italy and Spain
\nactually did something right. Because even though they had a sharper peak
\nand the total number of deaths, they also had a sharper decline. And then they had this overlaid with a chart of the
\nUnited States that has a milder peak and a milder decline. And I’m sure you’re thinking, wait a minute,
\nthis looks a lot like the flat and the curve argument that you were using back in March. And
\nnow you’re saying that this is wrong. It’s it’s it’s almost like this this internal disconnect
\nof where they’re searching for a justification for the lock down rather than making
\nthe lock down based on unsound science itself. Right. Right. And
\nhere you are again. Right. Because we we we are in the middle of June now. And and places like Texas
\nwhere I live, where we are, did not have a first wait. So Texas, I think the reality is that you are
\nnot talking about a second wave. We just did now have a very mild number of cases in Texas, throughout
\nTexas, those the state the lowest death per capita of any significant state. Right.
\nAnd then guess what? Yeah, we we move out of a two month lockdown to say, listen, we can just hold on
\nthe line here. So let’s say you don’t have enough cases to keep shut down forever into a vaccine comes up. It’s not an option.
\nRight. It’s not. It’s been it’s been it’s been opened up and life goes back to normal. If you look at
\nall the ability data. Texas seems to be back to a normal level of activity.
\nAnd fair enough. There’s some claim that there’s the growth in cases of gross
\nhospitalizations which weren’t expected. Right? That’s exactly. There’s no fighting that. That’s just. No,
\nno, no. Avoiding that. You see, again, I think a lot of the same
\ntype of rhetoric of like, well, see, that was a mistake. To open it up was like, well, what was the alternative? Right.
\nYou see all the alternate. The alternative across a
\nsimilar comparison you see between Europe and the US. Now you see the hard hit blue states
\nin the Northeast. They did arrive like, wait a second. New York has a thousand. That’s four million.
\nTexas has 70. Right. But anyway, I’m not saying that Texas
\nhas to get to a thousand. I think now we learned so much more. Right. So I actually just moved it to that part that we did
\nall day. Now we’re here in June and we learn a lot more about uncertainty. We had a beginning that
\nmight have been uncomfortable and B, politicians sort of, you know,
\npanic and opt for lockdown type strategy. What do we know now? How do you see what we know now
\nand what we’re going to go doing moving forward? What was the biggest point of data that’s been added
\ninto our our mix, or I guess more so confirms the early signs of it was just how disproportionately
\nthis disease affects the elderly. And it seems even to the point. So it’s a very severe
\nrisk among the elderly. It’s a very mild risk among younger people
\nand healthier people and of the younger people that have died from it.
\nThere’s overwhelming evidence that there are people with other medical conditions that exacerbate it,
\njust tragic and horrible in its own right. I don’t want to downplay that, but we
\nvery clearly know that this is a disease that disproportionately affects the elderly.
\nWhat does that tell us? What it tells us that that maybe we need to consider different approaches to our policy
\nresponses, that we’re not there back in March, the foremost among those being the nursing home
\nissue. So I’ve done a little bit of data work. Unfortunately, the states are not
\nvery good at keeping these records. But Massachusetts, which is where I live,
\nhas I gather that they actually have really robust daily data on
\nuncovered cases and the fatalities that come out of this. And they started back in early April. They started
\ntracking the daily deaths in nursing homes as opposed to the general population.
\nAnd they discovered something really quite early on. So back in April is about 50 percent of all deaths
\nin Cobbett in Massachusetts were in nursing homes, nursing homes that accounted for.
\nI think there’s a population of about 50 thousand people in a state of seven million is
\na tiny portion of the population, is accounting for half of all COVA deaths. And that number
\nis increased. It’s it’s now hovering around 63, 64 percent of
\nCOVA deaths in Massachusetts are from nursing homes alone. So this is telling me something very, very
\npronounce. It says that we not only have an acute vulnerability among elderly people,
\nbut it’s also an acute vulnerability among elderly people in a certain type of scenario,
\na certain type of facility. So that tells me you need a policy response, targeting and figuring out
\na way to lessen the susceptibility of nursing homes. And that could be
\nmaybe that could include you have tighter restrictions on entry and exit into them. Maybe
\nit could include spreading out or dispersing the population at the nursing home. A big one
\nthat’s come up. A lot of states adopted these these bizarre policies that require nursing homes to readmit
\ncoated patients and they end up being carriers that bring it into an otherwise secure facility.
\nAnd next thing you know, half of the residents have contracted the disease, all sorts of things
\nthat could have been done for nursing home homes and still should be done. But we’re basically omitted
\nfrom this general top down, one size fits all approach that we took back in March.
\nAnd then the final complication of this, you know, we talked about the Neal Ferguson Imperial College model,
\nwhich is all premised upon agent based interactions as a simulation model in
\nthe general population. What happens if we do business as usual and people contact
\nfive people in the course of their day going to work and school and stuff? We closed down work. We closed
\ndown school. Maybe that reduces to 2 and that limits the number of interaction. Such it’s a
\nsophisticated model, but it’s a general population model. You go back to the 2005 paper
\nwhere they first developed this model and they say quite explicitly, we do not account
\nfor group residences such as nursing homes, prisons and care facilities.
\nSo the model that we based, all this policy response on this, the sophisticated
\nagent based simulation for the general population, did not even account for the one area
\nthat’s turned out to be far and away the single biggest cause of Copan deaths.
\nAnother way of saying that is the model that Neil Ferguson and the Bureau College used. It predicted
\na certain type of deaths, but they are not the types of deaths that we are actually see. So
\nthat tells me maybe we need to step back and reevaluate even that even the core
\nempirical basis that we made these decisions on and and taking new knowledge,
\nabandoned lockdowns entirely and went to a more focused policy that looks at nursing homes,
\ndeals with the nursing home problem. Nursing home problem is a very, very sad one, because
\nit does seem. Nobody in the Western world has been able to deal with acts correctly like
\nLittle Sweden. You know, I think the response to it has been very measured. But they did a bad job with our
\nhomes. I feel like in Germany, pretty much every country outside of Asia
\nwhere I don’t think they have this, the system of nursing homes are parents. Right. Yet somehow.
\nI don’t know. It’s hard to say the exact understand why the death rate in Asia was not as bad as in the Western
\nworld, but it’s just like tragic. But but that tells us about the different
\nresponse moving forward here. Exactly. For sure. And and
\nso you’re also talking about one thing that you talked about, wrote about it is about
\nthe idea of going back to do. Ferguson, unfortunately, is claiming victory, somehow claiming that
\nthe treatment, in fact, that I would like to call of of the lockdown. So so
\nsome folks will go out there and say the lockdown saved 3.1 million people. And that was the right
\nshot of of the nature, sort of. I wouldn’t call a paper. It could be the blog
\nthat was supposed to be. How do we think about that? Yes. Yes.
\nThis is a major problem with the epidemiology literature. And this is something I love as a social scientist, just
\nfor an intellectual exercise to look at this stuff, even though the implications are very profound.
\nSo one of the great problems of empirical social sciences, as you referred to, is
\ninferring causality, detecting causality. It’s really easy to
\nfigure out that two things correlate with each other to trends to correlate. But but proving that one caused
\nthe other is such a statistical conundrum. And this is something that social
\nscientists have been working on for millennia. If you figure out how
\ndo you track whether one. Variable
\ncauses the other to happen. How do you separate these two things? And the statistical tools
\nthat we have for inferring these are very sophisticated, but they’re also relatively
\nin their infancy. This is something that emerged basically in the late 20th, early 21st century,
\nthe most advanced causal inference techniques that we have. And they’re very prominent in political science and economics.
\nSome of the physical sciences as tools that are just now starting to be explored. But if you look
\nat the epidemiology literature, it’s almost non-existent. They aren’t using causal inference.
\nSo that that’s the study if you want to go our blog posts in nature. What they did is
\nthey took their own model, the Imperial College model, and they projected the number of deaths
\nthat they assumed would happen without the lockdowns. And then they just take they should do a suppression exercise.
\nThey take the difference between that, the actual deaths we have. They say, aha, we saved three million people
\nor we saved two hundred thousand people here and there. That’s not social science.
\nThat’s about the same level of sophistication as when Donald Trump
\ntweeted before March 20, 20 years as the stock market’s rising, therefore, I caused it.
\nSo that’s the problem when I see this is a major deficiency of the epidemiology
\nliterature right now I’m working on a separate paper. It’s kind of a review of all the studies
\nthat claim to prove the lockdown effectiveness to see if they have a causal inference mechanism
\nin there. Do they use difference in difference? Do they go even a little more sophisticated,
\nuse like a synthetic control to create a counterfactual that they can judge something against?
\nAnd very, very few papers have even awareness of that. And the ones that do, I think
\nare been to two or three synthetic controlled studies so far. One was on Sweden
\nand its conclusion was that the lockdowns did not work. They were ineffective. The counterfactual was
\nno different than what Sweden happened. The other was on Wisconsin that looked at the date
\nwhen the Wisconsin sharp. There was a sharp discontinuity in the policy. So you got to a treatment
\neffect on an exact date. Easy to isolate. We can see if there’s a spike
\nin deaths afterwards. And quite the opposite. Wisconsin’s been on a continuous downward
\ntrend since then. So the only studies that have really looked at this that have robust causal
\ninferences are coming up with the exact opposite conclusion from these mathematical models
\nthat just assume by subtracting reality from their own projections that they must have caused it.
\nAnd the other thing missing and all those is going to be also the fact that it’s not enough to look at at a point in time
\nbecause of luck, even if they’re locked down, even if I give you the lock down, somehow stop
\nall disease spread while he gets to introduce and later on.
\nSo so so the only way that I think lugged down could effectively get rid of of of
\ndebts would be by locking down entirely without an interactions and to have an effect that might seems
\npretty tantalizing, which is just not saying that simple, implausible. So do you once
\nyou look over a period of a year or Xie’s are you? That’s when I
\nthink you going to be able to say something more clear by, let’s say, the Sweden experiment versus the others going to native countries,
\nfor example, or any of that. That’s that’s that’s not
\nyet available. But but it’s it’s unfortunate how how the claims are too quick because they’re
\ncoming in and trying to justify new lockdowns coming about. Right. So
\nthey’re using that information. They see Texas should lock down now because that’s the only way
\nto save lives. It’s like this bizarre default position that we always go to the lockdowns.
\nHere’s another discovery that just kind of shocked me. This was a big aha moment when working in some of
\nthis literature. You know, I’m an economic historian by background and training. I like
\nto look at past events to compare to that’s both hundreds of years ago where recent history
\nand I found this paper came from four leading epidemiologists at Johns Hopkins
\nUniversity. It was published in 2006. And this was in the midst of some of the influenza
\ndiscussions that were happening around them. They were also really concerned about bioterrorism. So like
\nan outbreak of al-Qaeda using chemical or biological agents
\nto cause an attack is a big, big point of attention. But they asked the Johns Hopkins team,
\nwhat are the policy interventions in this paper that summarize? Basically, all the literature
\nhad a stunning conclusion in it. It said that wide-scale what they called were quarantine’s or what we
\ncall lockdowns. Now the language is shifting. It’s a wide-scale society. Wide quarantines
\nor lockdowns should be taken out of consideration entirely because
\nthere’s no evidence that they work. This is all just theoretical models. We don’t
\nhave any clear cases from past pandemics. There’s no natural experiment that has shown
\nthese things are justified. It’s all just existing in this theoretical world of epidemiology
\nmodels that have just assumed it to be the case. So this
\nis kind of a stunning revelation. It shows that not only are critics of the lockdown existing
\noutside of the field of medicine and epidemiology, there are internal critics
\nto epidemiology who have been saying quite consistently for some time that
\nwe don’t really know what we’re doing here when we’re enacting these policies. And yet you have
\na political culture, a media culture, basically public choice of facts. What is the media like
\nto do? What’s the same thing that happens? yaller in Texas. You know, hurricanes on the Gulf Coast. What happens whenever
\nthere’s a hurricane in the Gulf? CNN and the whole news crew descends on the coast
\nand are there standing out in the middle of the storm trying to predict the disaster.
\nThe media is very, very susceptible to hype. It’s very susceptible to alarmist claims.
\nAnd walked aliens are very synchronised to addressing alarmist claims. So they that
\nthey tend to get preferenced in the public discussion by
\nreporters, news crews that are all about showing the flashy shot of the
\ndisaster happening and not really interested in the measured policy discussion that needs to take
\nplace to deal with something like this. So let’s start to talk a little bit about speculate, I guess,
\non on what why? Why is it that that I feel that our elites fail as
\nmiserably because the media is doing what they do, which is like if you beat your leads. Right. That’s what they
\ndo. And that’s always been unfortunate. It’s not a new phenomenon. I think maybe
\nnow we’d like to talk about social media being like a sort of potential lies in more.
\nThat’s sort of like feeling the flames that that that media likes to look at clickbait
\nto someone. But that’s not only a new phenomenon with social media. But the elites
\ntypically are more into measured responses for water pollution crises.
\nAnd, you know, any other measure of political. Any idea of
\na quality choice? So what happened? Why is different here? Why? Why? You know, I would blame
\nour field as well as academics. Economists. I think were incredibly silent
\nat first. They did a lot of good work after in terms of like, oh, can I work with, you know, condoms are better
\nmodelers and epidemiologists so quickly like models that like to see what actually is a little weird.
\nBut the first time in the beginning when lockdowns had been considered, it was silent deciles
\nacross that leads. And what other Republican governors or Democratic governors, they locked everybody
\ndown. Think one stood out, which is South Carolina governor. Right. Right. Yeah. Very few.
\nSo why how is that? How do we get there? Yeah. So I keep going back to
\nit in my mind, as we look across both the physical and social sciences we have been dealing
\nwith, something is referred to as the replication crisis in data for decades.
\nThis is a well-known phenomenon. It exists in in economics, biology, physics,
\nyou name it. And this is the problem of of a wheat’s intellectual elites in the academy
\npublish work, including in top journals, that other scholars
\ncome back and they just try to replicate it, trying to run the same test. And they find out based on the available information,
\nit can’t be done. And there’s been very little correction that’s taken place on that.
\nI think this is a problem across multiple fields, but it really kind of burst
\ninto the open at the outset of the coffee crisis because everyone was like, okay, well this
\nis the epidemiologists turf will defer to them. We’ll let them
\ncall the shots early on. And we’re going to sit back. We’re going to have to wait to weigh
\nin. And part of that said, there there’s some humility built into that. But but the problem
\nhere is when you have epidemiology, it turns out to also be afflicted by a replication crisis of its own,
\nalso be afflicted by bad data of its own that basically just let it run
\nwild with some some really suspect work. And, you know, I hate to keep harping
\non the the Imperial College model. But, you know, it’s the major model that’s driving
\nworld decisions. It’s the one that other countries are replicating. And it turns out to
\njust be a bad model. Just be very ill suited for the situation
\nthat we encounter. And yet there was like this deference that we have to give it over to the epidemiologists
\nto run with it, complicate that even further. We seen this in the subsequent
\nmonths as there’s almost been like a self-inflicted discrediting of the science by
\nall these changing standards. The changing goalposts we were talking about, even if anything,
\nas simple as advice. You know, we heard the other day, Anthony Algy basically admitted that the government
\nlied about bask- recommendations back in March, February and March.
\nThey were discouraging people from buying NASA, not because that was what
\nthe medicine said, but they were trying to preserve the masks supply for the hospitals and allies, saying, oh, now
\nwe we have enough masks. We can reverse courses. That may be some sort of
\na political a shrewd political trick. It does not help the condition
\nof science in the public’s mind. It undermines the respect
\nand trust that we were supposed to place and people that are exercising expertise and we see this
\nup and down the epidemiology profession. The other one that’s come out is when we had the BlackLivesMatter
\nprotests pop up. And I think there are some there are some very legitimate
\ncauses that people are protesting here, that the murderer, George FOID, is horrific.
\nAnd I think almost all Americans were in agreement that it was horrific. It reflects a real
\nserious, substantive problem with our police culture that I support addressing
\nthat. I think most people, most reasonable people think that we need some sort of solution, too.
\nNonetheless, those protests burst out into the open and kind of an uncontrolled way. They kind of happened
\nin defiance of the lockdowns. And very unexpectedly, it’s a spontaneous emergence.
\nWell, what did the epidemiologist do in response to that? The very same people who were scolding
\nand finger wagging at just everyday life actions
\nof going to your barber shop or going out to the store in public only two weeks prior
\nchanged their tune. They became political messengers rather than scientific messengers
\nand started carving out all of these like really tendentious excuses of why the current
\nwave of protests was not as vulnerable to takeover it as
\nas we had all been. Just two weeks prior. So they sent a very inconsistent message there,
\neven to the point where some of the most prominent epidemiologists at University of Washington
\nand Yale University were did two very pronounced outspoken epidemiologists
\nthat had been up until just a few days before the Black Lives Matter. Protests broke out,
\nscolding people for violating the lockdown for going out in public. This is a time
\nof a public health crisis and we must all do our part. But they politically sympathized with the
\nBlackLivesMatter protest. So just in the course of a couple of days, they completely flipped their message.
\nAnd I you know, I’m not judging sympathizing with BlackLivesMatter as a protest, cause that’s valid.
\nBut I would judge a scientific expert who is predicating his or her
\nown advice and whether we should shelter in place strictly on whether they politically
\nagree with the reason to violate the shelter-in-place or not. And again, Nate,
\nthe interesting part of it is like the movement being very critical of the way we force rules.
\nYeah. Yes. We’ll meet a half weeks prior to say, you know, us have these rules that have to be forced
\nby police. Right. Right. And you saw that. So before George Point,
\nthere was another kind of viral video that circulated and it was of an African-American man
\non a bus in Philadelphia. And he wasn’t wearing a mask, even though there was no no rule in place.
\nSaid he had to wear a mask, but some transit police decided don’t. You’re not wearing a mask. You
\nneed to go and dragging the guy off the bus and slamming him to the ground. It’s horrific to
\nwatch. You’re sitting there thinking, wait a minute. This is actually the same problem. And
\ntwo weeks ago, you all you epidemiologists were okay with this type of enforcement.
\nAnd now you’re saying that you recognize the problem. That’s it’s telling me that your message
\nas a scientific community is not rooted in science at all. So let’s
\nwrap up by again speculating about the future here. Yeah. So
\nit seems to me that that this episode is going to have a lot of
\nconsequences to a lot of our institutions. And yes, there’s all the obvious financial
\nconsequences to all of us are going to suffer. And universities are in dire trouble. I see I see
\none of your books about cracks in the ivory tower. And I really enjoyed the book, even though it’s very critical
\nof my business. You’re justifiably critical of my business. So that’s that’s great.
\nThere’ll be a lot of adjustments and things that we’re gonna have to do. And
\nbut I think the two institutions that I’m thinking about this a science generally speaking, I started doing your first or just science
\ngenerally speaking and and governance, the fact that we all of a sudden realized that
\nsafeguards that we had about about dictatorial powers that come in, you know, that we don’t allow
\natoms. Have they had it by just calling an emergency? Right. Call it an emergency.
\nApparently can put me on house arrest for three weeks. Three months. I basically sent a suspension of our democracy
\neffectively. Effectively. Right. And so policies with
\nenormous consequences there were not legislated. There were just done by fiat by 50 executives
\naround the country. So I you know, those two things I think are gonna have
\na huge rediscovery about it. So how do you see the playing out? What’s what’s your.
\nWell, again, it taps into the extent to which scientific expertise has
\nengaged the self-inflicted wound. They’ve discredited themselves as it’s almost with the old story
\nabout the boy who cried wolf. The next time when the wolf actually comes,
\npeople are going to be much more distrusting of that level of expertise, because
\nI think that I think it’s pretty clear now that the scientific
\nadvisors, as well as the politicians overplayed their card. They overreacted with the general
\nsweeping policy nationwide when it should have been a much more targeted policy around
\nnursing homes and other things that we can maybe effectively control. So
\nI see a lot of mistrust emerging in the future, too, invocations
\nof scientific expertise as the basis of emergency. And that can have problems in both
\ndirections. It can have problems in both undermining future attempts to
\nenact similar policies. But it can also make people more skeptical and more distrust distrustful when
\nan actual emergency does happen, a much more severe, whether it’s a pandemic or natural disaster,
\nit’s that they’ve kind of painted themselves into a corner now where their credibility is on the line
\nand and people really don’t know where to go to to trust it. And then
\nthe second thing, and this is what really alarms me is that I think some of the way
\nthat the protest to the book, both of BlackLivesMatter
\nand the lockdowns themselves have played out, have unfortunately
\nincentivized people of authoritarian stripe on both the far left
\nand the far right to really enter into the political dialog. These are people that are
\nreacting to heavy handed enforcement across a multitude of policies,
\nbut they’re reacting in such ways of either saying, well, we need the police to crack down even harder
\nor we need to engage in violence to topple the system or topple
\ntypes of government that we don’t like. Those types of reactions, I think are fundamentally authoritarian
\nand premised on extremism. And I think a very unfortunately, the way
\nthat this whole thing was played out, it’s almost like the politicians
\nthought that they could carefully measure and implement a lockdown
\nand keep extending it, extending extending it. And there’d be no consequences that we get to
\nthe. Were we very lightly and in a managed way remove it? Well, that’s not playing out at
\nall. People are are basically discarding the lockdowns on themselves because they
\nthey were extended far beyond what people were willing to countenance. And now it’s kind of a chaotic
\nstructure and response in that chaos is unfortunately open the door to some very ugly authoritarian
\ntendencies. One of the one of the things that from the very beginning, I think one of the Swedish epidemiologists,
\nif you ever watch some of the interviews of their interviews, is I recommend, because they were they were
\nvery level headed from the very beginning. One of the things in these old gentleman said a couple of times is that
\nI don’t understand what’s the plan that any of these countries are locking down have to unwind the lockdown.
\nExactly. Exactly. There’s no data that will allow them to see. OK. Now we can open it up because there will
\nbe no data on that unless a vaccine comes about. And and I think, again, that’s sort of
\nbeyond the data on the scientific side, the aspect of social interactions and arrest that might
\ncreate and be creative. What is the plan? What is it? How do you do this? Very difficult. And we’ve
\nseen that not only here, but pretty much everywhere. Absolutely. Absolutely.
\nPhil, thank you so much for this. Thanks for all the writing you do on lots of topics of the day. You particularly
\nhave been very used, but you’ve been read your columns the a few months and hopes to Texas
\nat some point. Not so distant future. Yeah, exactly. Exactly. Thanks again.
\nThanks for listening to Policy McCombs.<\/p>\n"},"episode_featured_image":false,"episode_player_image":"https:\/\/podcasts.la.utexas.edu\/cepa\/wp-content\/uploads\/sites\/21\/2021\/05\/SC_PolicyMcCombs_Art-scaled.jpg","download_link":"https:\/\/podcasts.la.utexas.edu\/cepa\/podcast-download\/549\/phil-magness-covid-19-interview.mp3","player_link":"https:\/\/podcasts.la.utexas.edu\/cepa\/podcast-player\/549\/phil-magness-covid-19-interview.mp3","audio_player":null,"episode_data":{"playerMode":"light","subscribeUrls":{"apple_podcasts":{"key":"apple_podcasts","url":"","label":"Apple Podcasts","class":"apple_podcasts","icon":"apple-podcasts.png"},"google_play":{"key":"google_play","url":"","label":"Google Play","class":"google_play","icon":"google-play.png"},"google_podcasts":{"key":"google_podcasts","url":"","label":"Google Podcasts","class":"google_podcasts","icon":"google-podcasts.png"},"spotify":{"key":"spotify","url":"","label":"Spotify","class":"spotify","icon":"spotify.png"},"itunes":{"key":"itunes","url":"","label":"iTunes","class":"itunes","icon":"itunes.png"}},"rssFeedUrl":"https:\/\/podcasts.la.utexas.edu\/cepa\/feed\/podcast\/policymccombs","embedCode":"
Phil Magness \u2013 COVID-19 Interview<\/a><\/blockquote>