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 few errors.

  • 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 country.
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.

Alternative Measures:

  • 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 from poverty.
  • 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 number.




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 considerations.
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.
Alternate Measures:

  • 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.

Average Income
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 poverty increases.
Incorrect Central Tendency: When talking about income we want to know what is typical. But median is the correct measure of typical, not average.
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.
Alternative Measures:

  • 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.
Alternate Measures:

  • 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.
Alternate Measures:

  • 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.

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