Posts in Three Lines, Coronavirus Edition

I haven’t been able to write as much about the current situation as I would like to.

Personally, I am doing fine. My wife and I are lucky to have some of the most secure jobs in the country – we are both public university professors — and we’re all healthy and as comfortable as can be expected under the circumstances, and we have access to outdoor space. But we also have two children who are now home all day, both of them young enough to need more or less constant attention. And I’m teaching three classes this semester, and the transition to teaching online, which I’ve never before done, has been challenging. And of course there is work already in the pipeline that has to be completed, like my project with Andrew Bossie on the economic mobilization of World War as a model for today. (The first part is here and the second should be out soon.)

I don’t mean to complain. Again, my personal situation under the lockdown is fine. But I do feel bad about not being more present in the important moment for economic debate since 2008-2009, if not longer.  There’s an endless number of urgent, challenging, and profound economic questions to be wrestled with. I’m a bit jealous of people like my associates Nathan Tankus and Jacob Robbins, who are in a position to give the economic situation the attention it deserves and putting out a steady stream of excellent posts on their respective blogs.

And to be fair, it’s not just a matter of time. Like, I suppose, most people, I don’t feel any confidence about how this situation will develop, or what the right framework is to think about it through. I feel I’ve spent many years developing a set of economic ideas and arguemnts with, and within, certain positions, that may not be relevant here. I find it hard to gather enough thoughts together to be worth writing down.

If I did feel able to blog regularly about the economics of the coronavirus, here are some of the posts I might write. I don’t claim these are the most important topics, just ones that I would like to blog about. 

Taking the money view. Our economy consists of a network of money payments and commitments, many of them corresponding to some concrete activity. What’s unique about this crisis is that the initial interruption is to the concrete activities rather than the money payments. This complicates the policy response: It’s not enough to inject more spending in the economy somewhere, on the assumption that it will diffuse through the normal circuits of income and expenditure, we have to think about maintaining the payments, and social relations, associated with various specific activities while the activities themselves can’t take place. 

Paging Henry George. While much of the concrete activity we think of as “consumption” is on hold, much of consumption spending is various forms of social overhead that has to happen regardless; housing is far the most important category here, with a large fraction of mortgage and especially rent payments already not being made. We urgently need to replace these payments with public money, or else suspend (not just defer) them in a controlled way; the flipside of this is that here as elsewhere, where private payments are replaced with public ones, there’s an opportunity to transform the social relations structured by those payments. In this case, that could mean not just replacing rent payments  but buying out properties, so as to replace private ownership of rental housing with public or resident ownership.  

Crying “fire, fire” in Noah’s flood. Despite the uniqueness of the current crisis, I still think one important dimension is a shortfall of demand. While many businesses have been directly shut down by coronavirus restrictions, it’s clear that many others are limited by a lack of customers – airlines traffic are down by 95% not because airlines can’t find people to staff the planes, but because no one is buying tickets. (The planes that do fly are empty, not full.) As incomes continue to fall from unemployment — and only a fraction are replaced by UI and other forms of public assistance — the demand shortfall is only going to get deeper, so I’m a bit puzzled when people like Dean Baker say that the problem  in the coming year might be too much spending rather than too little.

The skeleton of the state. One reason I’m confident that the economy is going to need more demand, is that recessions always involve a downward spiral between income and expenditure; once economic units have run through their reserves of liquidity, and/or start changing their beliefs about future income, the fall in spending will continue under its own power, regardless of what started it. One important area where this process is already underway is state and local governments; thanks to a combination of institutional constraints political culture, spending here is even more closely linked to current income than it is for households and businesses. In the last recession state and local spending continued to fall for a full five years after the official recovery.

Credit contraction? Adam Tooze, in the LRB, describes the economic crisis as “a shockwave of credit contraction,” which sounds to me like an uncritical updating of the 2008-2009 script for 2020. Is there any evidence that limits on borrowing are currently playing an independent role in reducing activity, or are likely to in the future? The problem seems obviously to be a collapse in current income, not a sudden unwillingness of banks to lend. 

Send in the Fed. Even if a credit contraction is not a factor in the crisis, it doesn’t follow that efforts to boost the supply the of credit are irrelevant. Easing credit conditions can help offset declining demand from other sources — that’s monetary policy 101, and especially true in the current crisis, where so many incomes need to be temporarily replaced. It’s very important, for example, that the Fed support borrowing by state and local governments, partly because they may be finding it harder to borrow, but mainly because they should be borrowing much more.

Pay as you go vs prefunding. As everyone knows, state and local governments face many constraints on their ability to borrow, which the Fed can relieve only some of. But another important margin for state and local government is on the asset side; it’s not widely recognized, but in the US, subnational governments are substantial net creditors, which in principle allows them to fund current spending by reducing net asset acquisition. One important way that they can do this is by suspending pension fund contributions — this may sound crazy, but what’s really crazy is that they prefund pension expenditure in the first place.

Can we blame the shareholders? A number of us have observed over the past decade have observed that the marginal use of both corporate profits and borrowing now is payouts to shareholders; this, along with the activities of private equity, have left a number of corporations with high debt and weak balance sheets even after years of high profits. There’s an argument that this has left them more vulnerable to the crisis than they needed to be. I’m not entirely convinced on this, especially given that the relevant counterfactual seems to be higher real investment and/or higher wages rather than simply accumulating liquid assets, but it’s a question very much worth exploring.

Seasonal disorder. One technical but, I think, important point about all kinds of economic data right now is that we should not be using seasonally adjusted numbers. Seasonal adjustment for unemployment claims, and for many other economic variables, is based on the percentage change from the previous month, which  produces totally spurious results in the face of the kinds of moves we are seeing. For example, new unemployment claims rose by 3 percent, from 6 million to 6.2 million, from the week ending April 7 to the week ending April 14; but since claims normally increase by 7 percent between the first and second week of April, this was misleadingly reported as a decrease of 4 percent. 

The opportunity to be lazy. This fascinating review of a book on the plague in 17th century Florence quotes a wealthy Florentine who opposed the city’s policy of delivering food to those under quarantine, because it “would give [the poor] the opportunity to be lazy and lose the desire to work, having for forty days been provided abundantly for all their needs”. It’s striking how widespread similar worries are today among our own elite. It seems like one of the deepest lessons of the crisis is that a system organized around the threat of withholding people’s subsistence will deeply resist measures to guarantee it, even when particular circumstances make that necessary for the survival of the system itself. 

Tracking COVID-19

FIGURES UPDATED 3-15-20

You’ve probably seen various graphs online showing the increase in coronavirus cases in various countries.

I don’t know that I am adding any value here, but I decided to reproduce those graphs using the convenient data from Johns Hopkins Coronavirus Research Center. (I can’t vouch for  the reliability of the Johns Hopkins data, but it seems to be what most news organizations are relying on.) Here’s one showing cases for all countries other than China that have reported at least 100 cases. The x axis is days after the 100-case mark was reached.

 

What we see here is that most countries show a consistent 35-45 percent daily growth in reported cases. Only Japan, at 20 percent, and Singapore and more recently Korea, at around 10 percent, depart significantly from this. It’s also interesting how stable the growth of cases in Japan has been over the past three weeks.

Now here is the same figure, but for US counties that have reported at least 10 cases. The x axis here is days since the first day with a least 10 cases. [UPDATE: I have stopped updating this graph since the Johns Hopkins site now reports cases only for US states.]

What surprises me here is that we basically see the same ~40 percent daily growth rate in cases. I would have thought that given all the insitutional differences and issues around testing, the US picture would have looked somehow different. But it seems like we might reasonably extrapolate from the international experience, that New York or Seattle could reach 10,000 cases in the next two weeks.

I have no expertise whatsoever on infectious diseases, so I am not going to say anything else about this.

Anyway, here is the R code if you want to produce figures like these from the most current data on the Johns Hopkins site. They also give latitude and longitude for every place included, so it wouldn’t be much more work to make an interactive map. Could be an interesting excercise for anyone teaching a statistics or data science course.

UPDATE: I had to change the code because Johns Hopkins is no longer reporting data for US places other than states. Here is the equivalent state-level figure. As you can see, the states with significant numbers of cases all show the same 40 percent daily growth rate.

 

#install.packages('reshape2')
#install.packages('ggplot2')
library(reshape2)
library(ggplot2)

corona <- read.csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv',
                   stringsAsFactors = F)
state.abbrevs <- read.csv('https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv', stringsAsFactors = F)
corona[,-1:-4] <- apply(corona[,-1:-4], 1:2, FUN=as.numeric)
US <- corona[corona$Country.Region=='US', ]
USplaces <-  US[grep(',',US[,1]),][,-2:-4]
USstates <- US[grep(',',US[,1], invert=T),][,-2:-4]
USstates <- USstates[grep('Princess',USstates[,1], invert=T),]
states.temp <- USstates
for (i in 1:nrow(USstates)){
  abbrev <- state.abbrevs[state.abbrevs[,1]==USstates[i,1],2]
  rows <- grep(abbrev, USplaces[,1])
  states.temp[i, -1] <- colSums(USplaces[rows, -1])
}
states.temp<- states.temp[!is.na(states.temp[,1]),]
USstates[,-1] <- USstates[,-1] + states.temp[,-1]
corona <- rbind(corona, c('', 'United States', NA, NA, colSums(USplaces[,-1])))
corona[,-1:-4] <- apply(corona[,-1:-4], 1:2, FUN=as.numeric)
china <-  corona[corona$Country.Region=='China',-2:-4]
corona <- rbind(corona, c('', 'China', NA, NA, colSums(china[,-1])))
corona[,-1:-4] <- apply(corona[,-1:-4], 1:2, FUN=as.numeric)
countries <- corona[corona$Province.State =='', c(-1, -3:-4)]

makePlot <- function (x, threshold){
  cases <- redate(x, threshold)
  data <- melt(cases, id.vars=1)
  names(data) <- c('place', 'day', 'cases')
  
  p <- ggplot(data, aes(x=day, y=cases, group=place)) +
    geom_line(aes(color=place))+
    geom_point(aes(color=place, shape=place))
  p <- p + scale_shape_manual(values=1:length(levels(as.factor(data$place))))
  p <- p + theme(axis.text.x = element_text(angle=45))
  p <- p + scale_y_continuous(trans='log10')
  p
}

redate <- function (x, threshold) {
  out <- data.frame(place='', stringsAsFactors = F)
  values <- matrix(nrow=99, ncol=99)
  n <- 0
  for (i in 1:nrow(x)) {
    v <- x[i,-1][x[i,-1] > threshold]
    l <- length(v)
    if (l > 1) {
      n <- n + 1
      out[n,1] <- x[i,1]
      values[n,1:l] <- v
    }
  }
  values <- values[!is.na(values[,1]),]
  cols <- length(colSums(values, na.rm=T)[colSums(values, na.rm=T) > 0])
  values <- values[,1:cols]
  out <- cbind(out, values)
}

makePlot(USplaces, 9)
makePlot(USstates, 10)
makePlot(countries[countries$Country.Region != 'China',], 99)