Weak Economic Measures
We need to use numbers to evaluate the economy, or our
own places in the economy. However, we need to understand what numbers
can tell us, and more important, what the numbers can not tell us. Even
though many economic measures exist, the errors we make when reading
economic measures typically result from just a few incorrect notions.
The many misleading conclusions we hear typically result from the same
Unreasonable expectations - Most often, we
simply expect numbers to mean more than they actually can. We believe
that numbers contain information that they actually don't contain.
This error is not intrinsic to the numbers themselves, it results
from our desire to have a simple answer.
Oversimplification - Any time we take a large
amount of data and reduce it to a single number, we lose information.
When we oversimplify information to a single number, we typically
lose the very information we want to discuss.
Range and distribution overlooked - Quite frequently
the most important information is the range or distribution of the
data. But range rarely gets reported, because range can not be reduced
to one easy to report number.
Misleading reports of central tendency - Most
reports we hear involve averages. But, averages usually don't contain
the information we want to know. Sometimes median does. Sometimes
other measure does.
Below, we look at how these errors cause most of the commonly reported
economic numbers to be misleading to most people. We will also suggest
alternative measures that would be less misleading for the desired results,
or provide more information.
Last Updated: January 2011
Unreasonable Expectations: We want GDP to tell us how well the
populace is doing, and how the citizens' lives have improved. However,
we have seen, especially in the "jobless recovery" of 2002 and
the bailouts of 2008 and 2009, that GDP can rise while our standard of
living fails to rise, or even falls. The total amount produced in a country
is not a reliable measure of the quality of life for the citizens of that
Oversimplification: GDP can also rise from increased production
of items that represent a drop in our standard of living such as security
equipment, prisons, intoxicants, and military. GDP can rise from increased
dependency on material goods or fall when community and family support
increases. Total production contains many hidden dimensions that do not
correlate with quality of life.
Overlooked Distributions: If a large portion of the production
goes to a small portion of the citizens or if a significant portion of
the economy does not really support our quality of life then GDP fails
to be a reliable measure of our quality of life. We saw this during the
first decade of the millennium. The GDP increased while wages for the
vast majority of the populace stagnated. Most of the increase went into
military production, insurance inflation, and executive compensation.
Only a small part of the increase went to the general population. GDP
does not distribute equally, or even equitably, throughout the population.
Thus, GDP is not a good measure quality of life for the population.
- GINI - GINI is a measure of economic inequity. It does a better job
of telling you whether the wealth of the nation is actually going to
the citizens. However, it does not tell you how many citizens are suffering
- Wealth concentration (GDP per populace segment) - Wealth concentration
would better inform us whether the rise in GDP actually reaches the
citizens of various income levels. However, Wealth concentration may
be reported for all status levels, so it can not be reduced to a single
Unreasonable Expectations: We frequently want to use the unemployment
rate as a measure of how good our government is. But most of the factors
that make up unemployment are only indirectly correlated to our government.
We want the unemployment rate to tell us whether the economy is creating
enough opportunity for all of us, but the measure omits many important
Oversimplification: Unemployment reduces a range of variables into
one single number. Long term unemployment and long term underemployment
are both much more serious than short term unemployment. But estimates
of unemployment not only fail to make this distinction, they frequently
fail to count the long term unemployed, while successfully counting the
most of the short term unemployed. The number also fails to inform us
as to why the people are unemployed. The measure of unemployment includes
too many variables to provide consistently meaningful data.
- Total employment - Employment is a better measure than unemployment
because it contains fewer variables that need to be measured and calibrated,
and fewer variables that are inversely related to the economy.
- Workforce utilization - Various measures of workforce utilization
exist. Some do a better job of accounting for long term unemployment
and underemployment than the popular unemployment measure.
- Total weeks of unemployment - A measure that adds up the total weeks
of unemployment for the populace at any given time would be far more
informative since long term unemployment is a more significant measure
of the weakness of the economy. However, because of the integral nature
of this number, it changes slower, and would not measure abrupt changes.
Unreasonable Expectations: We want average income to estimate our
standard of living. But standard of living is not measured well by money.
Community, trust, love, stability, health, freedom - all correlate weakly
to money, yet all are key elements of a good life.
Oversimplification: Measures of average income contain hidden variables
that work inversely to quality of life. Increases in incarceration, unemployment,
death rate and inflation rates can all increase the measure of average
income. Yet all represent a drop in quality of life. Average income is
strongly influenced by the income of the wealthiest 1%, and weakly influenced
by the income of the bottom 30%. Thus average income can rise even while
Incorrect Central Tendency: When talking about income we want to
know what is typical. But median is the correct measure of typical, not
Overlooked Distributions: The range, or distribution, of incomes
tells us more about our economy than either the average or the median.
Distribution of incomes or wealth includes information about poverty,
equity, and opportunity. Average does not.
- Median income - Median tells us how the people in the center are doing.
Average does not. However, median only tells us about the center, and
does not tell us about those at the bottom. If we are using income as
a measure of opportunity, those at the bottom matter the most.
- Percentile income - Reports showing the range of income by percentile
would be much more informative. However, since percentile is about range,
reporting it requires graphs or multiple numbers. Range reports can
not be reduced to single numbers.
- Leisure time - What people really want, and need, is leisure time
- time with their family and community, time to take care of their personal
needs. But many studies have shown that Americans are working longer
hours, commuting longer hours, and taking less vacation time than they
did 40 year ago. We have more stuff, but less time for our families
and our personal needs.
Growth in Federal Spending
Unreasonable Expectations: People want a single number to express
how responsible or efficient government is. But total spending is not
an accurate measure of either responsibility or efficiency.
Oversimplification: This measure does not consider what problems
government needs to solve and how those problems change over time. Not
only does this measure fail to measure changing needs, it fails to consider
changing population. The number also fails to consider changing wealth
(i.e. ability to pay) of the population. Typically, federal spending gets
reported without factoring in inflation.
- spending per citizen adjusted for inflation- spending both supports
citizens and is paid for by citizens, so spending growth must be compared
to population growth
- spending per GDP - spending is limited by GDP
- spending by agency - what problems are we spending the money on? If
the challenges increase, the spending will also. If the challenges decrease,
the spending should also.
Profit (Quarterly & Percent)
Unreasonable Expectations: We want profit to give us an accurate
representation of how well a corporation is doing, and how strong they
are. But profit does not directly measure these things.
Oversimplification: Reported profit is largely a bookkeeping game.
A company may hide profit in unnecessary expenses. Buying out a smaller
company results in a real wealth increase, but gets reported as an expense
and resulting drop in profit on the books. Executive salaries are listed
as expenses even though they are frequently where a large portion of the
real profit goes. Companies will play games to hide profit to reduce their
taxes. Conglomerates have historically chosen to take a loss on the books
in one sector of a company to reduce the tax liability in another sector.
The profit necessary for survival does not scale linearly with the size
of a company. A small two person company making only a 10% profit might
not survive. A large multinational company pulling a 2% profit has plenty
of room to spare.
- Jobs created - The ability of a company to create jobs tells us more
about its success and potential than does profit.
- Median employee income - The ability of a company to provide its workers
a good wage also tells us a lot about the company's success and potential.
- Executive compensation to worker compensation ratio - If this ratio
is high, then something is going poorly at the company. In fact, at
least two studies showed that executive salary tends to correlate negatively
with company success.
We have looked at just a few examples of how we let numbers mislead us.
Typically, the numbers are not wrong; we simply expect the numbers to tell
us more than they reasonably can. This usually results from accounting methods
that oversimplify information into a single number, while washing out the
information we really want to know.
Above, we just gave a few common examples. You should learn to ask the same
for all reported numbers. How have I expected too much information from
this number? How does this number oversimplify the concepts? What information
about range and distribution was washed out in the averaging process? If
you regularly ask these questions, you will not be deceived by numbers.