When we estimate a Phillips curve, the relationship between inflation and unemployment, we expect
to find a negative coefficient on inflation, but instead we often find that this coefficient
is significant and positive.
Furthermore, if we estimate a simple OLS autoregressive distributed lag specification of the kind proposed
by William Niskanen in his 2002 Cato Journal article, we find that the coefficient on current
Inflation is negative, consistent with the original Phillips curve and its Keynesian
interpretation, but coefficients on lagged inflation are positive and significantly larger.
This result can only be explained by Austrian business cycle theory. Inflation lowers unemployment
in the short run while raising it and by a greater amount in the long run. The lowered
unemployment initially brought about by inflation, the traditional Phillips curve, is the unsustainable
Model Expansion Phase of the Austrian Business Cycle.
The higher unemployment which follows is the process of malinvestment liquidation which
cannot be explained by any other model of the business cycle.
Niskanen's results were criticized to the effect that he should have estimated vector
error correction models.
We start by examining the consumer price index in a simple OLS regression where it is explained
by Various Monetary Aggregates. These have very high R squares. Because all the data
here are non-stationary, the estimates are super consistent, though residual tests suggest
the monetary data should be put in logarithms. Recent empirical findings that monetary aggregates
don't actually contribute to forecasting future consumer price index, combined with the fact
that they do contribute here to explaining the current CPI, suggest that prices merely
rise contemporaneously with the money supply.
These residuals suggest structural changes with a marked increase in short-term volatility
after 1995.
In general, to overcome this problem, regressions and error correction models were estimated
over truncated samples.
Estimating a Phillips curve vector error correction model for 1948-2010, in the cointegrating
In the following equation, the coefficient on inflation is positive and significant,
meaning that higher inflation means higher unemployment, and the intercept suggests a
natural rate of unemployment of about 4%.
This result is robust to changing the lag specification in the disequilibrium adjustment
process, truncating the sample range and adding the three-month Treasury bill interest rate.
It could be questioned whether an error correction model is appropriate, however, because all
All the data are stationary, that is, they don't have a long-term trend.
The disequilibrium adjustment process, assuming 1 to 24 months of lag changes in unemployment
and inflation, shows that every 1% in inflation raises unemployment by just under 1% after
for years and this permanent increase in unemployment starts after about 14 months.
Truncating the disequilibrium adjustment process to 6 to 18 months results in a slightly smaller
total increase in unemployment with an earlier onset, but this seems to be an artifact of
removing part of the actual process of adjustment.
Adding the short-term interest rate gives the same result.
Note that over the first two years, higher interest rates, looking now at the lower of
these two graphs, lower unemployment slightly.
This seems to be because the monetary expansion which results in inflation is implemented
through targeting lower interest rates.
This outcome is also robust to truncating the disequilibrium adjustment process.
Now those were all over 1948 to 2010, over the period of 2003 to 2010, every 1.57% increase
in inflation has resulted in a 4% increase in unemployment, an even less favorable trade-off.
The period of onset of increased unemployment is also shortened.
This is hardly changed by truncating the disequilibrium adjustment process.
Next we examine a vector error correction model of two non-stationary processes, which
are the kind of data that vector error correction models are really intended for.
And these are civilian unemployment and the consumer price index.
The two underlying data sets are variables that the unemployment rate and the inflation
rate depend on.
The cointegrating equation here gives us a positive and significant coefficient on the
consumer price index, suggesting that a rising consumer price index accompanies higher employment
in equilibrium.
Now for the first error correction model, lower unemployment was good.
Over 1948 to 2010, the disequilibrium adjustment process indicates a loss of 160,000 jobs for
for every one point increase in the consumer price index.
And here, as opposed to the earlier vector error correction model, the fact that it goes
down indicates that when you raise the consumer price index, you lose jobs.
Residual plots suggest structural changes in the CPI process, which is the one on the
in the bottom, but not in the employment process, which is probably the more important one,
which is the one on the top, because that's what we care about explaining.
Over the 1982 to 2010 period, every one point increase in the CPI costs over 200,000 jobs,
and it gets worse.
Over the 1990 to 2010 period, every one point increase in the consumer price index costs
There's over 480,000 jobs, almost half a million, but things aren't all.
There is a silver lining.
Over the 2003 to 2010 period, the most recent experience, every one point increase in the
consumer price index costs a mere 409,000 jobs.
Is this just an artifact of a combination of fixed weight CPI recently understating
price increases and some actual price deflation which occurred during the recession and was
is fairly unique to this period in the data set,
or is the Keynesian resurgence actually improving the economy?
No, it's not.
Employment and MZM, money of zero maturity,
are non-stationary variables for which vector error
correction model is also appropriate.
Increases in MZM lower employment
in the cointegrating equation.
This is the opposite of what was found with the consumer price
Index.
The coefficient is negative and significant
and is robust to shortening the sample range.
In the disequilibrium adjustment process for 1959 to 2010,
every $1 billion increase in MZM wiped out 2,500 jobs
after four years.
Residual plots suggest structural instability
in the MZM equation, but not in the employment equation.
And generally, the interpretation
One would be that MZM is more exogenous than employment, which depends on it.
Since 1982, now this is with a truncated sample, every $1 billion increase in MZM has destroyed
4,125 jobs.
Since 1990, every $1 billion increase in MZM has destroyed nearly 10,000 jobs, and things
These do get a little better.
Since 2003, every $1 billion increase in MZM has only cost us 6,350 jobs.
Another attractive vector error correction model to examine is in employment, the monetary
base and the Austrian money supply.
The monetary base is not statistically significant in the cointegrating equation, but the Austrian
And money supply is always negative and significant.
Over the 2003 to 2010 period, in the disequilibrium adjustment process, every $1 billion increase
in the Austrian money supply wipes out approximately 5,000 jobs.
And that's very consistent with what we found with MZM.
These residuals don't suggest structural breaks, but there's less volatility in the 2009 to
2010 period.
This relationship between employment, unemployment, output, income, and general economic activity, on the one hand, and the money supply prices and inflation, on the other hand, is dramatically different from what has traditionally been supposed.
The conventional view of the Phillips curve that there is a trade-off between inflation and unemployment is true at times, but is strictly a short-run phenomenon.
Sometimes this relationship is reversed even in the short-run. It seems to be always a negative relationship in the long-run, the long-run meaning six to 48 months or longer, and a six-month long-run is not very long.
That inflation decreases unemployment in the short run is consistent with the
unsustainable expansion phase of Austrian business cycle theory. The much
stronger and more robust finding that inflation increases unemployment and by
much larger amounts over the longer run can only be explained by Austrian
business cycle theory. This is an important result which strongly disconfirms all
other models of the business cycle and this result is highly robust from a
from a statistical perspective.