Predicting recessions with payroll and unemployment data

Recessions are notoriously difficult to measure (even the NBER occasionally gets it wrong) and an official declaration of a recession may be lagged by more than 6 months. Economist Claudia Sahm devised the Sahm Rule, using changes in unemployment levels, as a more timely predictor of recessions.

Sahm rule: US Data

But the signal repeatedly lags the official start date of recessions by several months, limiting its usefulness for investment purposes.

In previous articles I observed that payroll growth is a good predictor of recessions. But payroll growth has been declining for decades; so it has been difficult to devise a one-size-fits-all-recessions rule. Until I turned to using momentum.

Twiggs Momentum is my own variation on the standard momentum formula and I applied this to monthly payroll data to arrive at a 3-month TMO.

Sweden: Sahm rule

The orange band on the above chart reflects the amber warning range, between 0.5% and 0.3%, where recession is likely. If TMO crosses below the red line at 0.3%, risk of recession increases to very high.

When the TMO falls below 0.5%, a recession is likely, but there is one false reading at 0.49% in 1986. So I treat 0.5% as an amber warning level.

There are no false signals below 0.3% in the last 50 years. So I treat the 0.3% level as a red warning — that recession risk is very high.

Some of the signals (e.g. 1975) are late but the TMO has a far better record, than the Sahm Rule, at giving timely warning of recessions.

The August 2019 TMO reading is an amber warning of 0.5%.