Editor's note: unemployment rate forecasts will be published monthly on the Brookings on Job Numbers Blog. Forecasts have been published monthly starting in October 2012. Read past editions: 10/12, 11/12, 12/12, 1/13, 2/13, 3/13, 4/13, 5/13, 6/13, 7/13, 8/13, 9/13, 10/13, 11/13, 12/13, 1/14, 2/14, 3/14, 4/14, 5/14, 6/14, 7/14, 8/14, 9/14, 10/14.
Our method is based on a very informative but under-analyzed part of the monthly employment report: the worker flows taking place in the labor market.
The U.S. labor market is dynamic: Millions of people move between Employment (E), Unemployment (U), and Not-in-the labor force (N). Since there are three different labor market states, the labor market is characterized by six flows: from unemployment to employment (U to E) when an unemployed finds a job, from employment to unemployment (E to U) when a worker is laid-off, from unemployment to out-of-the-labor-force to unemployment (U to N) when an unemployed leaves the labor force, and so on.
The rate at which people are moving into and out of unemployment tells us a great deal about the current state of the labor market and its likely future developments.
A simple analogy helps explain the reason why. The unemployment rate can be thought of as the amount of water in a bathtub, a stock. Given an initial water level, the level of the water in next period is determined by the rate at which water flows into the tub from the faucet and the rate at which water flows out of the tub through the drain. When the inflow rate equals the outflow rate, the amount of water in the tub remains constant. But if the inflow rate increases, we know that the water level will be higher in the future. In other words, the inflow rate and the outflow rate provide information about the future level of water—or in this case, unemployment.
This insight forms the cornerstone of our approach. Namely, we exploit this convergence property, whereby the actual unemployment rate converges toward the rate implied by the labor force flows, also called the “steady-state unemployment rate.”
Our paper shows that our model has consistently yielded more accurate unemployment predictions in the near-term (basically over a 6-month horizon) than either professional forecasters or other econometric models.
To facilitate analysis and discussion of real-time developments in the labor market, I will publish updated forecasts from our model on the BPEA web site each month following the release of the employment report. I will also write a post discussing the forecast and our model’s reading of the latest unemployment numbers.
To read more about the underlying model and the evidence that it outperforms other unemployment rate forecasts, see Barnichon and Nekarda (2012).