American counties worst affected by coronavirus as of 3/20/20

 The chart shows the ten worst affected counties in the US, with badly hit areas of China and Italy included for comparison.

The worst hit region in the US is the central Colorado ski region around Aspen and Vail Colorado. This is shown in the chart as Eagle County, Gunnison County and Pitkin County in the state of Colorado. Although the absolute number of cases is under 100 these are small towns with a very high per capita rate of cases.

The next worst hit area is the New York Metro area with Westchester County as the worst affected region. New York City, Nassau and Bergen county New Jersey are also in the New York metro area.

There are also nasty outbreaks around New Orleans (Orleans Parish) and Seattle (King and Snohomish Counties). Things have slowed down in the San Francisco Bay Area over the past couple of days, so no Bay Area counties make the chart.

In my opinion, there is a good chance that the outbreaks in New York and New Orleans become as severe as the outbreak in Northern Italy.

Coronavirus travel bans work: How China contained the virus

China was fairly successful in containing the worst of the coronavirus outbreak to Hubei province.The chart below shows the case rates per million people for all the provinces in China

The case rate per million in Hubei went as high as 1145 per million people. No other Chinese province exceeded 22 per million people. This degree of containment was a huge success for the Chinese authorities. In my opinion, it was due to an unprecedented travel ban introduced on January 22nd for the city of Wuhan. This was expanded next day to cover 35 million people in Wuhan and the surrounding cities. US experts interviewed  at the time were skeptical that the travel ban would work, but in hindsight it seems to have been a big success.

Also important for containment was the vigorous public health response in other Chinese provinces. The travel ban ensured that other regions were not overwhelmed by infected people coming out of Hubei.

Reducing the load on the health care system seems to have had a big impact on the death rate. The death rate was 4.5% for Hubei province but only 0.9% for the rest of China.

The US is currently doing a much less effective job of containing the virus than the Chinese did. After Hubei, Zhejiang was the worst affected Chinese province with 21 infections per million people. Several US states are now above that level.

However, I think there is still time to avoid severe, widespread infection by imposing travel restrictions on the worst affected states. Seattle, the San Francisco Bay Area, the New York City Metro Area, Louisiana, Colorado and Massachusetts are the worst affected areas. Shutting down passenger air and rail travel from those regions could prevent them from sending large numbers of infections to the rest of the US.

NY Times January story on Wuhan travel ban

Government bond yields and trade

Most discussions of trade imbalances focus on the desirability of the goods that a country produces. Countries with trade surpluses are believed to be those that produce highly desirable goods.

However, trade imbalances are linked to capital flows. What if it is the demand for capital flows that drives the trade deficit, rather than the other way around? Capital flows from regions of surplus, where interest rates are low, to regions where interest rates are higher.  Government bond yields provide a measure of the demand for capital.

The chart below provides some evidence for this point of view. It shows that trade surplus countries tend to be countries where government bond yields are very low, indicating a surplus of capital.

The lowest bond yields are in Switzerland and Germany , while the highest are in Australia, Italy and the US.

Data note:  Government bond yields are for the 10 year bond on 5/31/2017. The data are from the Bloomberg or Trading Economics website.  The current account balance data is from the OECD stats website.

Home ownership and trade

There appears to be a negative correlation between the home ownership rate and the current account balance for advanced economies. Below is a chart showing the data.

Here is the same data plotted in a way that makes the correlation more obvious.

I have a couple of theories about why these two variables are linked.  Countries with high home ownership rates may have policies that make it easy to get mortgages and other forms of consumer credit. This leads to strong demand for savings which tend to drive capital inflows and associated trade deficits.

Another theory is that home ownership reduces the need for cash savings in bank accounts. People who own houses never have to pay rents and they don't have to worry about inflation in housing costs.  Less bank saving is offset by capital inflows and associated trade deficits.

Which American President produced the best growth in incomes?

To answer this I will plot the change in real median personal income for each President's time in office. I'm plotting the fractional change in income from the year before the president entered office.  A fractional change of 1.2 corresponds to 20% income growth since that president entered the White House.

The winner here is Bill Clinton, with Ronald Reagan a strong second.  This chart clearly shows that some Presidential terms are much better for workers than others.  Obama's performance was very poor in his first term, and he is going to finish well behind Reagan and Clinton.

Below I show the same data displayed in a different way. Real income growth in 2015 was the strongest in the past 40 years. Another few years of that kind of performance would be very helpful for American society.

How do modern Presidents compare with those from the 1950s and 60s like Eisenhower, Kennedy and Johnson? The real median personal income data only goes back to 1976, so I went looking for another income data series. The best I found was real compensation per hour for the non-farm business sector.  The deterioration in performance from the era of Eisenhower to modern times is really quite shocking. There is a big step down in growth after 1970 and another one after 2008.

This measure shows that President Obama has had the worst growth in real hourly compensation of any President since at least 1953. Real hourly compensation data looks worse for Obama than real median personal income. Hourly compensation is linked to wages, while personal income includes investment income and other income sources as well as wages.


Notes and data sources

1/ All data is from FRED. I am using the non-seasonally adjusted Real Median Personal Income in the United States    series set to show percent change from a year ago.  I get non-farm real hourly compensation from here.

2/ 'W' is George W Bush who was President from 2001 to 2008. 'Bush1' is the first President Bush who served from 1989 to 1992.

3/ Carter served from 1977 to 1980. Reagan served from 1981 to 1988.

4/ 'Real' income shows growth in purchasing power after inflation is taken into account. 'Median' income is more relevant to the middle class than average income, which is increased by rising incomes among high earners. 

Some pictures from the John Muir Trail in Yosemite National Park

Last week I hiked a short section of the John Muir Trail in Yosemite National Park. These pictures are from the Lyell Canyon area south of Tuolumne Meadows.

This area is within a few hours of the road, so no overnight camping is required.

A relationship between inflation and growth in wages and employment for the US economy

Some folks are speculating that the US economy is approaching full employment. In this post I'm going to explain why I don't believe that is so. I'm going to describe a Phillips curve like relationship between the core rate of inflation, wage growth and job growth. This is something of a work in progress. It seems to work well for the US economy, and a very similar approach appears to work for the UK, but I have not applied it to other economies yet.

On the y-axis I will plot the percentage growth in the product of wages and employment. For example, for January 1965 wage growth is 3.2% and employment growth is 3.6%. The quantity (wages*employment) grows by a 7% and this I plot on the y-axis.

The US economy seems to take time to respond to changes in employment and wages. On the  x-axis I will plot core inflation delayed by 21 months. For example, for the January 1965 data point I use the inflation from October 1966.

The best fit line shown on the chart predicts 2.2% inflation in October 2017 based on current rates of wage and job growth.  The growth in (wages*employment) is currently 4.4%, and it has never gone above 7% since 1992. In that time inflation has stayed under 3%.

Under what circumstances should we be concerned about a return of inflation? When inflation took off in the late 1960s the  growth of (wages*employment) was a little over 8%. From 1972 until 1981 it never fell below 7%. As long as it stays under 7%, inflation should stay low.

With growth in (wages*employment) at 4.4% as of January 2016, there is clearly a lot of room for stimulating the economy. When we approach full employment, wage growth should rise substantially.


 Notes and data sources

1/ All data is from FRED. For wages I am using the  "Average hourly earnings of production and non-supervisory employees: Total Private (AHETPI) " The data is seasonally adjusted. For January of each year I take the percent change from the previous year.

2/ For employment I am using "All employees : total nonfarm payrolls (PAYEMS)" seasonally adjusted. For January of each year I take the percentage change from the previous year.

3/ For core inflation I am using "Consumer Price Index for All Urban Consumers: All Items Less Food and Energy (CPILFESL)" I take the annual rate of change delayed by 21 months. For example, (earnings growth * employment growth) for January 1965 is plotted against inflation for October 1966.

4/ Care needs to be taken with the arithmetic. For example, wage growth of 3.2% is a factor of 1.032. Employment growth of 3.6% is a factor of 1.036. Earnings growth * employment growth = 1.032*1.036 = 1.069 which is equivalent to 6.9%.