Economic Strength Methodology
The formulas used to determine economic
strength measure how the economy has behaved, not what has caused it to
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
All Workers - Earnings
All Workers - Jobs
All Workers – Wages
Per Capita Total Worker Earnings
Per Capita Personal Income
Earnings by Place of Residence
Per Capita Earnings by Residence
Wage & Salaried Workers - Earnings
Wage & Salaried Workers - Jobs
Wage & Salaried Workers - Wages
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.)
Non Farm Proprietors - Earnings
Non Farm Proprietors - Jobs
Non Farm Proprietors - Wages
Construction - Worker Earnings
Construction - Jobs
Construction - Wages
Retail - Worker Earnings
Retail - Jobs
Retail - Wages
Group 3 – These
sectors are negative sectors. Growth in these reflect a poor economy.
Per Capita Income Maintenance (Welfare)
Actual Per Capita Income Maintenance (Welfare)
Per Capita Medical Assistance for the Poor - (Medicaid)
Actual Per Capita Medical Assistance for the Poor – (Medicaid)
“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 Ames, IA MSA had the 8th
fastest percentage growth rate (18.9%) in Per Capita Income Maintenance from
2007-2011 among the 366 areas. However, its Per Capita Income Maintenance
in 2011 was $429, ranking 362nd. While the rate of growth is
high, “twice nothing is still nothing.”
The reverse also occurs. The El Centro, CA
MSA had the 7th the
highest Per Capita Income Maintenance ($1,599) among the metropolitan areas,
but it growth rate of 7.9% was very slow, ranking 362nd.
Of course there are the extremes. The
State College, PA MSA had the lowest Per Capita Income Maintenance and the 6th
slowest rate of growth. The Flint, MI
MSA has the 6th
highest Per Capita and the 33rd fast growth rate.
If only these criteria were used, the MSA’s would be ranked in the
following order: State College, Ames, El Centro, and Flint.
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. (2010 data
was released by the Bureau of Economic Analysis in April, 2012)
Last five years: 2013-2009
– Weighted once.
Last ten years: 2013-2004
– Weighted twice.
Previous Ten Years: 2003-1994
– Weighted once.
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
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
The first graph depicts a Mythical Area, which had an Average Annual
Percentage Increase (AAI) in Construction Jobs of 2.3% from 2004 through
Mythical Area had a
2.3% increase in 2004.
In 2005 it again had a 2.3%
increase. Each and every year, the area increased exactly 2.3%. This means
construction employers, each and every year, increased the number of people
they employed by 2.3%.
As a result, by
averaging the ten-year history, the Mythical Area, obviously, had a 2.3%
average annual increase (AAI).
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 Fargo, ND MSA for the same element.
Fargo, during the same ten years had an AAI in Construction Jobs of 2.3%.
This rate of growth ranked 16th among the 381 metropolitan areas for
the ten-year period.
graph shows the percentage increase or decline each year. You can see the
rate of growth is not absolutely stable.
While over the ten years it averages 2.3%,
there are obvious fluctuations year by year. In 2004
there was a 8.6% increase in
construction employment. However, in 2009 employment declined
8%. In 2013 there was an increase of 6.8%.
the average of the annual increases is 2.3%. However, the rate of growth is
not nearly as consistent as the Mythical Area. The growth line is not straight
but goes up and down.
the rate of growth of Construction Jobs for Fargois not absolutely
consistent, it is considerably more stable than the
Hammond, LA metropolitan area.
and the Mythical Area, Hammond had an average annual increase
of 2.3% over the ten years. As you can see in the graph, the rate of growth
was extremely volatile.
In 2005, Hammond gained 14%
and the next year gained 28.2%. in 2008
it lost 4.5% and lost 18.4% in 2009.
Yes, the average of
all of these years is 2.3%. How it happened
is considerably different than
relying upon economic growth percentages is not sufficient in order to
determine the character of a local economy. Economic stability must be
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.