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• The cutback in retail sales also caused cutbacks in manufacturing, in inter-
national sales, and in shipping and transportation. These cutbacks reduced
the profits of shipping companies, railroads, airlines, and trucking com-
panies.
• The combination of job losses, foreclosures, and business shrinkage
lowered the stock market by unprecedented amounts, although a partial
recovery took place during the spring of 2009. Full recovery was not seen
until early in 2013.
• The reductions in retail sales, manufacturing, and transportation coupled
with job losses have seriously reduced tax revenues at town, state, and na-
tional levels. Almost every state and a majority of towns had serious
budget deficits in 2009. Some of these continued into 2013 due in part to
excessive largess in pensions and health care for retired government work-
ers.
• Due to high unemployment rates and numerous foreclosures, state and
municipal tax revenues continued to decline from about 2008 through
2011, but there were some increases in 2012.
• Attempts to increase tax revenues via “tax-the-rich” methods backfired
and caused reductions in tax revenues. The very wealthy are highly mo-
bile, own properties in several states, and have attorneys and tax account-
ants far more sophisticated than state officials. There have been no suc-
cessful revenue increases in any state that has attempted tax-the-rich pro-
grams. In spite of the failure of this method, many states and the federal
government continue to try and push through these programs without un-
derstanding that revenues will decline rather than increase.
• Attempts to tax internet sales by individual states (such as Rhode Island)
backfired and reduced tax revenues. This is because major internet
vendors such as Amazon and Overstock cut ties to Rhode Island compan-
ies, as will most of the other major players. The result is damage to Rhode
Island companies without any corresponding increases in tax dollars.
What is technically interesting about the dot-com bubble, the Great Recession,
and the housing bubble is that these issues could have been predicted and modeled
using a combination of historical data and predictive analytics. It does not require
really sophisticated math to predict that if more houses are built than there are
people to live in them, prices must come down. It is also easy to predict that when
home prices fall below average mortgages, there will be many foreclosures be-
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