Big Data. Bureau of Labor Statistics. Survey data. Employment Big Data. Those are all things that calculating worklife expectancy for U.S. workers requires. Worklife expectancy is similar to life expectancy and indicates how long a person can be expected to be active in the workforce over their working life. The worklife expectancy figure takes into account the anticipated to time out of the market due to unemployment, voluntary leaves, attrition, etc.
Overall the goal of our recent work is to update the Millimet et al (2002) worklife expectancy paper and account for more recent CPS data. Their paper uses data from the 1992 to 2000 time period. Our goal is to update that paper using data from 2000 to 2013. The main goal of the paper is to see if estimating the Millimet et al (2002) econometric worklife models with more recent data changes the results in the 2002 paper in any substantive way.
Our approach is two fold. First we matched the BLS data cohorts based on the Millimet et al. (2002) and Peracchi and Welch (1995) papers. In a nutshell the CPS matching routine involves matching incoming and outgoing cohorts across a given year. Once the data is matched, we then look at the work status of the individuals to determine if they were active or in active across the year that they were interviewed by the BLS. . We were able to create a match CPS data set of 201,797 individuals where as the Millimet et al. (2002) found 200,916 matched individuals.
|Table 1. Comparsion of CPS cohort matched data sets|
|Year||Millimet et al. (2002)||Steward and Gaylor (2015)|
The CPS data was matched using the algorithm similar to Millimet et al (2002) and Peracchi and Welch (1995). Households in rotation 1-4 were matched using the household identifier number to the same household in rotations 5-8 of the following year. Individuals had to have the same sex, race and be a year older in rotation 5-8 to be determined a match.