The views expressed here are my own, not those of my employers.
Summary
The 5th percentile, median and 95th percentile annual unemployment rate in the United States (US) from 1948 to 2023 were 3.54 %, 5.55 % and 9.02 %.
There has been a very weak upward trend in the annual unemployment rate in the US, with the coefficient of determination (R^2) of the linear regression of it on the year being 3.54 %.
The tail distribution of the annual unemployment rate in the US bends downwards in a log-log plot. Igot a similar pattern for the annual conflict and epidemic/pandemic deaths as a fraction of the global population.
For the Pareto distributions fit to the 10.5 % (8) rightmost points of the tail distribution in a log-log plot, the annual probability of this being:
At least 10 % is 2.04 %.
100 % is 2.11*10^-10.
I suspect many in the global catastrophic risk community underweight the outside view outlined in this post, and therefore overestimate the probability of massive unemployment.
Methods
I fit Pareto distributions (power laws) to the 2, 3, … and 76 (= 2023 − 1948 + 1) highest values of the annual unemployment rate in the US from 1948 to 2023. To do this:
I get the slope and intercept of linear regressions of the above sections of the tail distribution on the annual unemployment rate in the US.
Since the tail distribution of a Pareto distribution is P(X > x) = (“minimum”/x)^“tail index”, ln(P(X > x)) = “tail index”*ln(“minimum”) - “tail index”*ln(x) = “intercept” + “slope”*ln(x), so I determine the parameters of the Pareto distributions from:
“Tail index” = -“slope”.
“Minimum” = e^(“intercept”/“tail index”).
Then I obtain the annual probability of an annual unemployment rate in the US of at least 10 %, 25 %, 50 %, 75 % and 100 %. Note unemployment requires not only not having a job, but also being actively looking for one:
Someone in the labor force is defined as unemployed if they were not employed during the survey reference week, were available for work, and made at least one active effort to find a job during the 4-week survey period.
The 5th percentile, median and 95th percentile annual unemployment rate in the US from 1948 to 2023 were 3.54 %, 5.55 % and 9.02 %. The maximum was 9.71 % in 1982.
There has been a very weak upward trend in the annual unemployment rate in the US, with the R^2 of the linear regression of it on the year being 3.54 %.
The tail distribution of the annual unemployment rate in the US bends downwards in a log-log plot. Igot a similar pattern for the annual conflict and epidemic/pandemic deaths as a fraction of the global population.
Tail risk
My estimates for the annual probability of an annual unemployment rate in the US of:
At least 10 % range from 0.105 % to 6.54 %.
100 % range from 1.32*10^-88 to 0.00749 %.
For the Pareto distributions fit to the 10.5 % (8) rightmost points of the tail distribution in a log-log plot, the annual probability of this being:
At least 10 % is 2.04 %.
100 % is 2.11*10^-10.
I believe the rightmost points of the tail should get the most weight to predict tail risk, which implies trusting lower estimates more. On the other hand, I expect inside view factors related to transformative AI (TAI) to push estimates upwards. All in all, I suspect many in the global catastrophic risk community underweight the outside view outlined in this post, and therefore overestimate the probability of massive unemployment.
Probability of massive unemployment in the United States based on historical data
The views expressed here are my own, not those of my employers.
Summary
The 5th percentile, median and 95th percentile annual unemployment rate in the United States (US) from 1948 to 2023 were 3.54 %, 5.55 % and 9.02 %.
There has been a very weak upward trend in the annual unemployment rate in the US, with the coefficient of determination (R^2) of the linear regression of it on the year being 3.54 %.
The tail distribution of the annual unemployment rate in the US bends downwards in a log-log plot. I got a similar pattern for the annual conflict and epidemic/pandemic deaths as a fraction of the global population.
For the Pareto distributions fit to the 10.5 % (8) rightmost points of the tail distribution in a log-log plot, the annual probability of this being:
At least 10 % is 2.04 %.
100 % is 2.11*10^-10.
I suspect many in the global catastrophic risk community underweight the outside view outlined in this post, and therefore overestimate the probability of massive unemployment.
Methods
I fit Pareto distributions (power laws) to the 2, 3, … and 76 (= 2023 − 1948 + 1) highest values of the annual unemployment rate in the US from 1948 to 2023. To do this:
I get the slope and intercept of linear regressions of the above sections of the tail distribution on the annual unemployment rate in the US.
Since the tail distribution of a Pareto distribution is P(X > x) = (“minimum”/x)^“tail index”, ln(P(X > x)) = “tail index”*ln(“minimum”) - “tail index”*ln(x) = “intercept” + “slope”*ln(x), so I determine the parameters of the Pareto distributions from:
“Tail index” = -“slope”.
“Minimum” = e^(“intercept”/“tail index”).
Then I obtain the annual probability of an annual unemployment rate in the US of at least 10 %, 25 %, 50 %, 75 % and 100 %. Note unemployment requires not only not having a job, but also being actively looking for one:
The calculations are in this Sheet.
Results
Historical data
Time series
Basic stats
Linear regression on the year
Tail distribution
Tail risk based on Pareto distributions
Discussion
Historical data
The 5th percentile, median and 95th percentile annual unemployment rate in the US from 1948 to 2023 were 3.54 %, 5.55 % and 9.02 %. The maximum was 9.71 % in 1982.
There has been a very weak upward trend in the annual unemployment rate in the US, with the R^2 of the linear regression of it on the year being 3.54 %.
The tail distribution of the annual unemployment rate in the US bends downwards in a log-log plot. I got a similar pattern for the annual conflict and epidemic/pandemic deaths as a fraction of the global population.
Tail risk
My estimates for the annual probability of an annual unemployment rate in the US of:
At least 10 % range from 0.105 % to 6.54 %.
100 % range from 1.32*10^-88 to 0.00749 %.
For the Pareto distributions fit to the 10.5 % (8) rightmost points of the tail distribution in a log-log plot, the annual probability of this being:
At least 10 % is 2.04 %.
100 % is 2.11*10^-10.
I believe the rightmost points of the tail should get the most weight to predict tail risk, which implies trusting lower estimates more. On the other hand, I expect inside view factors related to transformative AI (TAI) to push estimates upwards. All in all, I suspect many in the global catastrophic risk community underweight the outside view outlined in this post, and therefore overestimate the probability of massive unemployment.