Q328. Have a look at this recent release from Bureau of Labor Statistics (BLS). The data separates those without a job into unemployed but "in the labor force" and "marginally attached to the labor force" and a subset of these called "discouraged" - the former would like to work but have not looked in the last four weeks and so are not counted as unemployed. The latter are not actively looking for work having given up on the idea that its possible to find. These groups are not included in the denominator when the unemployment rate is calculated. The simple version of the unemployment rate is, then,

(1)
\begin{align} UR = \frac {Unemployed} {Employed + Unemployed} \end{align}

Some recent op-eds have counseled caution about optimism that the overall unemployment rate has been going down because it might reflect growth in the number of people no longer looking for work. We'll think about that with a Markov model. We'll simplify the states a worker can be in:

  • employed (E)
  • short term unemployed - 14 weeks or less (US)
  • long term unemployed - over 14 weeks (LS)
  • Marginally attached to the labor force - no longer looking for a job (MALF)

Let's construct a simplified Markov model of unemployment based on transition rates shown here:

markov_transition_table.png

If the unemployment rate is calculated as the ratio of those who are short term unemployed (US) plus those who are long term unemployed (UL) to the total labor force (E + US + UL), how would things evolve over the next twelve months if the starting numbers are these:

markov_starting_point.png

What will the unemployment rate be? Even if it is agreed that getting unemployment to near 6% is a policy goal, are there reasons the results might not be a cause for celebration?

Create a chart showing changes over the next 12 months. Suggestion: plot total employment (E) on secondary axis since it's such a large number. In the alternative, put it on a separate chart.

Excel worksheet here