Sunday, July 31, 2011

California versus Texas:Part 5: Rural job losses and regional corrections

The Rural Population Effect

Studying the data I noticed that the states with high job growth all seemed to be highly urbanized. On the other hand there were states that seemed to be doing everything right, where the job growth was mediocre. The thing that stood out about those states is that they had large rural populations.

This chart shows job growth versus the percentage of the population which is urbanized. States with low urbanization never seem to do well. Highly urbanized states can do well, or they can do badly.

Over the past decade, rural areas seem to lose jobs at the rate of 1 job per 1000  residents per percentage point of rural population. So I would expect a state with a 40 percent rural population to lose 40 jobs per thousand state residents over the cycle from this rural population effect.

These rural job losses are consistent with what is known about strong productivity gains in industries like farming. I think that this is the tail end of a process that has been going on for over a hundred years. In 1880 agriculture accounted for over 41% of US employment. People had to live on the land they were farming, so a scattered rural population made sense. Today, only 1% of the US population works in farming, and a scattered rural population no longer makes economic sense unless commodity prices rise. For the past century, towns and cities have been the engines of the economy.

It is important to note that the Census Bureau's definition of urbanization includes some quite small communities. Anybody living in a town of more than 2500 people is an urban resident.

California is one of the most urbanized states, with 94% of the population living in urban areas. Texas is at a slight disadvantage, with 83% of the population urban.

Regional Corrections

When I was a kid, I had a small tear off calendar with a different humorous quote for each day. One that I have never forgotten was the definition of Flannagan's Finagling Factor. This is:

 'That quantity which, when multiplied by, divided by, added to, or subtracted from the answer you got, gives you the answer you should have gotten.'

I'm going to have to introduce a few of these to make the numbers work out, but I promise to only use them across broad geographic regions, and there are only 3. What I think they represent is job creating factors that my model doesn't include.

The first one is for Alaska. My model predicts Alaska should be losing lots of jobs, when in fact it is gaining a lot. I think this is because Alaska is a state with few people and lots of oil. This one is +116 jobs/ 1000 population.

The second one is for the Rocky Mountain west. This one is +18 jobs / 1000 population. This area includes Montana, Idaho, Nevada, Utah, and Colorado. This might have something to due with mining and high commodity prices.

The third one covers three regions. These are the West Coast, the states along the Mexican border, and the East Coast from Virginia north to New England. This one is +30 jobs /1000 population. This might have something to do with the growth of international trade. The states involved are Washington, Oregon, California, Arizona, New Mexico, Texas, Virginia, Maryland, Delaware, Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire and Maine.

These regional corrections are the final element. In my next post I will show the predicted job growth and compared it with the actual job growth.

Data on urbanization is from Table 27 of the Statistical Abstract of the US from the Census Bureau. I'm using 2006 numbers.)

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