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Economic Strength
Rankings
“Economic strength
is the long term tendency for an area to |
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POLICOM specializes in studying the dynamics of local economies. From its research, it determines if an area is growing or declining, what is causing this to happen, and offers ideas and solutions to communities to improve the situation.
POLICOM addresses the condition of an economy from the viewpoint of it's impact upon the “standard of living” of the people who live and work in an area. The economic strength rankings are created so POLICOM can study the characteristics of strong and weak economies. The highest ranked areas have had rapid, consistent growth in both size and quality for an extended period of time. The lowest ranked areas have been in volatile decline for an extended period of time.
POLICOM has created economic strength
rankings for all Metropolitan Statistical Areas and all Micropolitan
Statistical Areas. There are now 366 Metropolitan Statistical Areas. Micropolitan Statistical Areas did not exist prior to the new definitions. Once looked upon as quasi rural areas, a Micropolitan Statistical Area must have an urbanized area (city) with a population of at least 10,000 but less than 50,000 population. They must be at least one county and most are. The OMB has identified 574 MICROS in the United States. Approximately 11% of the nation’s population resides in the MICROS. The formulas used to determine economic strength measure how the economy has behaved, not what has caused it to perform. The following are the data sectors used to create the rankings. Group 1 – These sectors reflect the overall growth in size and quality. The “quality” of the economy is based upon what people earn, as this influences their “standard of living” more than anything.
Group 2 – These sectors reflect how the economy is behaving. Small businesses and the construction and retail industries are extremely reactive to the “flow of money” coming into an area. They typically grow or decline in direct proportion to the condition of the economy. There are, of course, exceptions. Areas which have become destinations for retirement age individuals will have high growth numbers in both construction and retail, while they might not have a strong economy. (No system is perfect.)
Group 3 – These sectors are negative sectors. Growth in these reflect a poor economy.
“Redundancy” and “counter balances” are built into the criteria which compensate for anomalies which might occur in one or two of the items. As an example, an area might have a very high percentage growth rate in Per Capita Income Maintenance. This might mean the economy is on decline. However, percentages are funny things and can sometimes be misleading. It is much easier for an area with a low basis to have high percentage increases than an area with a high basis. The Reno, Nevada MSA, from 1998 to 2002, had the 30th fastest growth rate in Per Capita Income Maintenance among the 361 METROS. However, it also has the 19th lowest actual amount. While the rate of growth is high, “twice nothing is still nothing.” The reverse is also sometimes true. The Stockton, California MSA ranked 354th in the growth rate, but has the 9th highest Per Capita Income Maintenance of all the METROS. There are, of course, the extremes. The Saginaw, Michigan MSA had the 53rd fastest growth percentage and also had the 20th highest actual amount. The Madison, Wisconsin MSA had a very slow growth rate (ranked 345th) and also has the 21st lowest per capita welfare. If only these criteria were used, the example MSA’s would be ranked in the following order: Madison, Reno, Stockton, and Saginaw. POLICOM is also aware of anomalies in labor data. It has found economies which are on decline sometimes have a very high growth rate in the number of people employed in Retail Trade. Retail is a reactive industry, which grows and declines in direct proportion to the condition of the economy. So how can retail jobs grow in a declining economy? It is because labor data counts full and part-time jobs all as “jobs.” This means two part-time jobs are counted as two jobs in the data. When an area is in decline, retailers switch from full-time workers to part-time workers. A small retailer might have four full time workers. If the retailer lays off three full time people but hires five part-timers to replace them, there is a statistical gain in the labor data of two jobs. The retailer now has six workers, in the data, which is a 50% increase from when he had four full timers. To counter balance or compensate, the total earnings and wages are included. Under the above scenario, both earnings and wages will decline; bringing down the area in the rankings. The average annual increase is calculated for each of the items for three time periods. (2007 data was released by the Bureau of Economic Analysis in April, 2009.)
The percentage increases are then adjusted mathematically for consistency. Data sectors which reflect wages are counted twice, giving equal emphasis to quality as to the growth in size. The growth rates are then ranked. The rankings are totaled. The areas are then ranked for economic strength based on their total overall rankings. Consistency of Growth Simply identifying the areas that have the fastest or slowest growth rates is insufficient when trying to determine the character of a local economy. The rate, consistency, or stability of the growth is equally important. Areas with unstable, boom and bust economies are difficult places to conduct business. Residents of these areas are subject to economic uncertainty and stress. A merchant may lease extra floor space following three or four great years, only to go bankrupt after a subsequent economic decline. Residents might make long term financial commitments based upon rapid increases in earnings and employment, only to loose everything due to a sudden downturn causing massive layoffs. To better understand the nature of economic stability, we will examine the consistency of the Construction Industry for three areas which had the same average annual percentage increase. The first graph shown depicts a Mythical Area, which had an Average Annual Percentage Increase in Construction Employment of 3.9% from 1991 through 2000.
This Area had a 3.9% increase from 1990 to 1991. From 1991 to 1992 it again had a 3.9% increase. Each and every year, the area had exactly a 3.9% increase. This means construction employers, each and every year, increased the number of people they employed by 3.9%. As a result, by averaging the 10-year history, the Mythical Area, obviously, had a 3.9% average annual percentage increase (AAI). Most importantly, the area had perfect consistency as depicted by the straight horizontal line on the graph. The flow of money into the area, which supports this industry, grew in an absolutely consistent manner. This is a perfect situation. However, this is myth, not reality. Let us examine the economic stability of the Minneapolis MSA for the same element. Minneapolis, during the same ten years had an AAI in Construction Employment of 3.9%. This rate of growth ranked 116th highest among the 363 metropolitan areas for this ten year period.
While over the 10 years it averages 3.9%, there are obvious fluctuations year by year. From 1990 to 1991, there was a 4% decline in construction employment. The next year a 4.9% increase. The next year an 2% increase, so on and so forth. For the 10 years, the average of the annual increases is 3.9%. However, the rate of growth is not nearly as stable as the Mythical Area. The growth line is not straight but goes up and down. While the rate of growth of Construction Employment for Minneapolis is not absolutely stable, it is considerably more stable than the Great Falls, MT metropolitan area.
As with Minneapolis and the Mythical Area, Great Falls had an average annual increase of 3.9% over the 10 years. As you can see in the graph, the rate of growth was extremely volatile. From 1990 to 1991, the Great Falls area lost 3% of its construction jobs and the next year gained 11%. Then it lost 6%, gained 16% and so on. Over the course of the ten years, these “ups and downs” average 3.9%, the same as Minneapolis. Obviously, simply relying upon economic growth percentages is not sufficient in order to determine the character of a local economy. Economic stability must be considered. To measure economic stability, the difference or deviation in each successive year's percentage of growth is calculated (absolute number) and averaged, creating the Average Deviation from Previous Year (DEV). To determine the measurable consistency of growth, the DEV is subtracted from the AAI. This number is used for POLICOM's economic strength rankings. Inconsistent economies area ranked lower than consistent economies.
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