Month: March 2020

Endless austerity, state and local edition

Brian Nichols of the essential Employ America has a useful, if depressing, roundup of the coming wave of state-local austerity. Some highlights: Ohio, Nevada and Pennsylvania have already announced hiring freezes; Ohio is also looking at a 20 percent across the board cut in state spending, while Virginia has canceled planned raises for teachers. Many cities, including New York, St. Paul and New Orleans, are laying off public employees. And as I noted in my last post, New York  State is planning to slash $400 million from the hospitals at the front line of the crisis.

This isn’t new. One of the many drawbacks of American federalism is that state and local government spending — which includes the great majority of public sevices that people use on a day to day basis — is distinctly procyclical. Following the 2007-008 crisis, austerity at the state and local level more than offset stimulus at the federal level. And it lasted much longer than the recession itself.

In fact, as my colleague Amanda Page-Hoongrajok points out, inflation-adjusted state and local final expenditure did not return to its 2009 level until 2019.1 On a per-capita basis, real state and local final expenditure is 5 percent lower today than it was at the bottom of the last recession.

Source

As we face the rising wave of public-service cutbacks, we need to be fighting on all levels. We need to demand a massive package of aid to state and local govrnments as part of Stimulus IV. We need to be pushing the Fed to do more to support municipal finances. We need to keep the pressure up on mayors and governors not to throw their hands up and wait for the feds, but to be creative in working around their fiscal constraints.2 And also, we need to keep in mind: As far as state and local spending is concerned, the Great Recession never ended.

Daily News Op-Ed: Why Is Governor Cuomo Still Trying to Cut Medicaid?

(My Roosevelt colleague Naomi Zewde and I have an op-ed in the March 26 Daily News, criticizing Governor Cuomo’s plans to push ahead with cuts to state Medicaid spending despite the epidemic.)

Last week, as the coronavirus shut down much of New York, the state announced a bold plan to drastically cut funding for the state’s hard-pressed health care providers.

That’s right: As the coronavirus crisis escalates across New York State, Gov. Cuomo is proposing to slash funding for those at the frontlines.

Specifically, the cuts come via the Medicaid Redesign Team, appointed last month by the governor with the charge of cutting $2.5 billion from the state’s annual health spending. These cuts will not only mean an even more overstretched health care system; they will mean lost jobs.

For example, $200 million is slated to be cut from Consumer Directed Personal Assistance (CDPA), which allows elderly or disabled New Yorkers to hire their own home care assistants. As a Daily News editorial recently noted, CDPA was responsible for 36,000 new private-sector jobs in New York City in 2019, a lion’s share of all such jobs.

The biggest savings come from across-the-board cuts to health care providers, including $400 million from the state’s hospitals.

Cutting health spending in an epidemic seems like obvious lunacy. But it’s even worse than it seems.

Since the start of this epidemic, nearly one in five American households have had their hours cut or been laid off due to the virus. In New York, Cuomo said that the state has “never seen such volume” of unemployment claims.

As the economy slides over a cliff, we desperately need to keep people employed so that they can pay their bills and keep local businesses running. The proposed cuts will not only kneecap our health care system, but they will also deepen the coming recession.

But don’t we have to do something about out-of-control Medicaid spending? No, we do not. Medicaid spending is already under control.

Over the past five years, Medicaid spending in New York has risen by a steady 4% a year — exactly the same growth rate the state’s economy has had as a whole. And thanks to the Affordable Care Act, the share of total Medicaid costs paid by the state has gone down.

The apparent Medicaid crisis is entirely of the governor’s own making. When an arbitrary “global cap” on Medicaid spending turned out to be unachievable, instead of accepting reality, the state shifted a portion of the bill from fiscal year 2019-2020 to 2020-2021. This created the illusion of a big rise in this year’s costs.

Not only are there no runaway costs to rein in, but health spending is also an important economic stimulus. About 13% of New Yorkers work in health care — more than in manufacturing and finance combined. New York’s hospitals are stable sources of employment in many communities where good jobs are scarce. While many of the state’s traditional industries are in decline, health care promises to be a growth industry in the 21st century — if its growth isn’t cut off by shortsighted cutbacks.

Cutting state Medicaid spending today would be especially perverse, as the federal government appears poised to pick up a larger share of the program’s spending, just as it did in the last recession.

When private sector spending falls in a recession, the role of government is to lean against the wind, and boost public spending to fill the gap. Fiscal stimulus is primarily the responsibility of the federal government, but a state as large and rich as New York should also do its part — especially if leadership in Washington is lacking.

In normal times, trying to balance the budget through Medicaid cuts would be a mistake. Today, it is economic malpractice.

Talk on the Economic Mobilization of World War II

Two weeks ago – it feels much longer now – I was up at UMass-Amherst to give a talk on the economic mobilizaiton of World War II and its lessons for the Green New Deal.

Here is an audio recording of the talk. Including Q&A, it’s about an hour and a half. Here are the slides that I used.

 

 

The big three lessons I draw are:

1. The more rapid the economic transformation that’s required, the bigger the role the public sector needs to take, in investment especially, and more broadly in bearing risk.

2. Output can be very elastic in response to stronger demand, much more so than is usually believed. There’s a real danger that over-conservative estimates of potential output will lead us to set our sights too low.

3. Demand conditions have major effects on income distribution. Full employment is an extremely powerful tool to shift income toward the lower-paid and to less-privelged groups, even in absence of direct redistribution.

EDIT: The underlying paper is being revised to update the lessons for the present in light of the fact that “the present” is now an acute public health crisis rather than an ongoing climate crisis. The first part of the new version is here. The rest will be forthcoming in the next couple weeks.

You can also listen to an interview with me on Doug Henwood’s Behind the News here.

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)