Getting Around the US Government’s Fake Economic Statistics

What This Article Covers

• Why is a causal forecasting a missed opportunity?
• Where does the real challenge lie in creating economic causal models?
• How does one get the right data for economic causal forecasting?
• Why have government economic statistics been so gerrymandered?

Background

One of the missed opportunities in forecasting I have discussed in the past is using macro-economic trends in order to adjust forecasts. This type of adjustment would be performed at the top-level of the hierarchy, and in fact could be done for all products, if the product database in question is sensitive to macro-economic factors. Most products are, with their being a wide variation with consumer non-durables being on one end of the spectrum, and housing related industries being on the other.

Where is the Challenge?

The challenge is certainly not in the technology side, as long as you have the right application. The best way to apply a top down macro-economic factor is by attribute. For instance, the overall product database can be coded something like the following:

1. Macro-Economic Affected Products
2. Macro-Economic Semi-Affected
3. Macro-Economic Unaffected Products

The macro-economic factor that is used (unemployment, GDP growth, housing starts, etc..) can then be applied to varying degrees in the first two categories, and not at all in the third category. This of course is only one example for demonstration purposes. There could be 10 different categories like this, and multiple macro-economic factors used. For instance if different macro-economic factors were used, the coding could be the following:

1. Unemployment Statistics Applied
2. GDP Growth Applied
3. Housing Starts Applied
4. No Macro-Economic Factor Applied

As soon as this coding is performed, the descriptions added would appear right in the application data view, so that it is easy to apply the increase or decrease though a top down forecast.

Getting The Causal Data

So, the technology exists to easily do this, however, the problem actually lies in getting the right data. This is because the data released by the government has been so adjusted for political purposes that it is no longer a reflection of what is happening in the economy. These are the descriptive statistics that are quoted in news programs that we collectively used to measure the health of the economy and even the fairness of the economy, and they have been helplessly jerry-rigged. A few examples are listed below:

• The government knows that the unemployment rate is a statistic with great implications. Therefore multiple administrations have adjusted the number from its original calculation to make it look as low as possible.
• Inflation controls the cost of living increases that are paid to employees by both the government and private companies. In order to minimize the cost of increases, institutional power (both public and private) has an incentive to use a calculation method that minimizes the inflation rate.

These are just two examples of manipulated statistics, but there are many others. In what appears to be an unending attempt to control public perception, there are few government statistics that have not been “adjusted” over the past five decades. If a forecast adjustment model is based upon false economic statistics, it will not be effective, and will gradually be seen as a non-value add. One wonders how many macro-economic adjustments to forecasts have been abandoned because of this simple fact. Walter J. Williams has the following story which describes this exact thing:

One of my early clients was a large manufacturer of commercial airplanes, who had developed an econometric model for predicting revenue passenger miles. The level of revenue passenger miles was their primary sales forecasting tool, and the model was heavily dependent on the GNP (now GDP) as reported by the Department of Commerce. Suddenly, their model stopped working, and they asked me if I could fix it. I realized the GNP numbers were faulty, corrected them for my client (official reporting was similarly revised a couple of years later) and the model worked again, at least for a while, until GNP methodological changes eventually made the underlying data worthless. Despite minor changes to the system, government reporting has deteriorated sharply in the last decade or so. - Walter J. Williams

This lead Walter to develop ShadowStats.com, which adjusts the government’s statistics to be realistic, rather than to meet political goals. A few examples of the differences between what ShadowStats.com calculates for major government economic statistics and the official government released statistics are listed below:

1. The unemployment rate has been at roughly 9% for some time. However, ShadowStats shows it as roughly 23%. This is due to a number of factors such as workers who leave the workforce because they have been looking for so long they are discouraged. US Government statistics do not count these people. In fact almost all the recent reduction from 9% to 8.5% unemployment was due to people leaving the workforce, yet it was still celebrated in the media. Politicians prefer to keep this number artificially low in order to decrease voter dissatisfaction.
2. The average inflation rate is currently shown as 4% per year, however ShadowStats estimates a full 2.5 percentage points higher, or 6.5%. Furthermore, ShadowStats shows this differentials for years. If a person made $50,000 in 2010, they would need to have made $53,250 in 2011 and $56,711 in 2012 in order for their standard of living to just be maintained. Most companies tend to give out 3% raises, so every year, unless a person is promoted, or moves to a new job, companies pay less for their employees, and a major way they do this is by colluding with the government (though various conservative think tanks such as the Heritage Foundation or the Hoover Institute) in order to keep the real inflation rate hidden.

Conclusion

US Government economic statistics have been so manipulated over time that they are no long a reliable gauge for the state or performance of the economy, and any company which desires to use macro-economic variables will need to pay a source like ShadowStats in order to get numbers that can be used in a causal model to adjust the product forecast.

References

http:www.shadowstats.com

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