A PRESIDENT FOR “THE LITTLE GUY”

9th January 2017

One of the many paradoxes of the Trump presidency is that, although he is one of the richest men ever to be elected president, he was elected by “the little guy”.  The opposite of being rich is being poor. Trump was not elected by poor people on average, but by poor areas of the USA. What do we mean by saying this?

Start with the map of the USA with all of its 3056 counties marked in red (won by Trump) or blue (won by Hillary). Judged by the number of counties won, it looks like Trump won by a landslide: he took 2584 counties, compared to only 472 for Hillary. We also know that Hillary won the popular vote even though she lost the Electoral College: she got 2.8 million more votes than The Donald. Thus the map above is deceptive, because it shows the land area of the USA, not its population.

So Hillary won the counties which had larger populations and denser populations. Researchers from the Brookings Institution took this analysis a step further by looking at the real 2015 GDP of each county. The size of each county in the figure below is proportional to its share of GDP. The 472 counties which Hillary won produced 64% of US GDP!

This is what we meant when we said at the outset that The Donald was elected by the “poor” parts of the US. Regardless of the economic status of Trump voters individually, they live in the counties with fewer people and less GDP. Hillary’s voters, by contrast, may be poor individually but they tend to live in urban counties with more people and more GDP.

We take this analysis a step further by adding in some data from a study by the non-partisan Economic Innovation Group (“EIG”) which looked at the geographical distribution of economic recovery between 2010 and 2014. The EIG study concluded that only one-quarter of all counties added new businesses as fast as the national average. Even more strikingly, the study found that twenty counties alone generated half of the USA’s new businesses in this period!

Not surprisingly, the list includes the counties with the largest populations in the USA, representing major metropolitan areas such as Los Angeles, Miami, New York City and Houston. What is surprising is that Hillary won eighteen of the twenty counties. She even won most of the urban counties in states that Trump won, such as Texas and Florida. The only two high-growth counties she didn’t win  were Maricopa County (Phoenix AZ) and Tarrant County (Dallas TX).

We heard the Trump supporters tell us they wanted to make America great again. Their geographical distribution suggests that they have in fact been living in the low-growth parts of America. This fits what we know about the last decade: large urban areas have enjoyed faster growth – and more evenly spread growth – than rural areas and small cities. In particular, both coasts have done much better than the “fly over” states in the middle. Metro areas like Seattle, San Francisco, Los Angeles, Boston, New York, Miami and Washington DC now have significantly higher average incomes (and minimum wage rates) than middle America.

We knew from the voting statistics that Hillary voters tended to be urban, non-Anglo and young. What we didn’t know is that Hillary voters accounted for most of the USA’s GDP and most of its recent business growth. This conjunction of facts suggests that it is going to be difficult for the Trump Administration to fulfil its promises to bring back good jobs – unless it moves the Trump voters to the coastal cities.

 

Q.E.D

 

REFERENCES

Economic Innovation Group. 2016. The new map of economic growth and recovery. http://eig.org/wp-content/uploads/2016/05/recoverygrowthreport.pdf. Accessed 04 January 2017.

Lowrey, Annie. 2016. 2016: A Year Defined by America’s Diverging Economies. The Atlantic 30 December 2016. https://www.theatlantic.com/business/archive/2016/12/2016-diverging-economies/511838/  Accessed 04 January 2017.

Muro, Mark and Liu, Sifan. 2016. Another Clinton-Trump divide: High-output America versus low-output America. www.brookings.edu/blog/the-avenue/2016/11/29. Accessed 04 January 2017.