Table 1: Regression of Increase in Unemployment:
The model is quite clear about how to avoid high
Second Quarter 2006 to Second
unemployment rates such as those experienced in the
Quarter 2011
inland region of California. If you can avoid a housing
Regression Statistics
bubble and do not have to cut government spending,
R Square
0.72
Adjusted R square
0.68
Standard Error
Observations
you can avoid an outsized unemployment rate. It may still be high, the rate in Texas is over 8%, but it will not be as high as currently being experienced from
0.013
Sacramento to El Centro.
37
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For a variety of reasons Texas avoided a housing bubble, but
Coefficients
Standard Error
t Stat
California seems to be more prone to bubbles, first dot-com
Intercept
0.122
0.035
3.516
stocks and then housing. In the last two recessions California
Relative Bubble Size
1.082
0.212
5.095
has fared worse than the rest of the U.S. due in large part
Texas dummy
1.171
0.264
4.434
to these asset bubbles. This tendency may be the nature of
Employment Drop
-0.885
0.127
-6.994
the California economy, or it may be facilitated by policy
Mining Oil and Gas
-0.102
0.050
-2.058
which allows for easy money mortgages and restrictions on
UVL Directional Traffic
-0.148
0.067
-2.209
building. In fact, for an entrepreneurial economy such as California, avoiding asset bubbles from time to time may be neither possible nor practical. But what we learn from this
Effect of Rising Unemployment What we are trying to explain is the increase in the unemployment rate from the second quarter of 2006
is that much of the unemployment differential (not the job formation differential) is explained by the fact that California had a particularly acute housing boom and bust cycle and not by some other unknown factors.
More to the Story
to the second quarter of 2011. Table 1 presents our regression results. The factors we analyze explain
But there is a caveat. The analysis included a Texas specific
about 72% of the increase, i.e., the R Squared value
variable designed to measure whether or not there was
is 0.72. What we find is that for every 1% increase in
some specific Texas effect, different from elsewhere. This
the bubble, there is a corresponding approximately
variable turned out to be significant and important. In
1% increase in the unemployment rate, i.e., the
other words, something else is also going on in Texas and
regression coefficient is 1.08. A 1% decrease in
our model does not capture it. In part this result may be
government and construction employment yields a
due to Texas catching up with migration over the previous
.885% increase in unemployment. The mining, oil
five years. When new migrants come in, whether they are
and gas measure does not have a straightforward
disaster refugees from Katrina, illegal immigrants from
interpretation as the other variables, but picks up the
Mexico, or displaced factory workers from the North and
impact of rising energy prices. A larger energy sector
Northeast, their demands for services are not instantly
is significantly correlated with a small reduction
translated into new jobs.
in the unemployment rate. Finally a 1% increase in migration into the state is associated with a .148%
For example, a school district does not ordinarily build new
decrease in unemployment.
schools and hire new teachers in anticipation of an increase
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