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

As for the results, overall there are several findings. First 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.

Overall we match their results very closely as well.  For example Millimet et al. (2002) found that a male who was 26 years old with a less than a high school education had a 27.27 years WLE remaining while we found that person had 26.319 years remaining based on our replication of their work. They found that the same age person with a high school had 32.89 years remaining while we found 32.728 years remaining. The replication was particularly good for both less than high school and high school levels of educational attainment.

The WLE  numbers are close but not quite as close for college and some college. This is primarily due to the fact that we use different definitions of some college and college then Millimet et al. (2002)  did in their 2002 paper

Table 3. Comparsion of Millimet et al. (2002) and Steward and Gaylor (2015) Active to Active Worklife Expectancy Probabilities
Millimet et al (2002) Steward and Gaylor (2015) Replication
Age Less than High School High School Less than High School High School
18 32.331 38.944 31.469 38.410
19 31.801 38.239 30.926 37.846
20 31.247 37.522 30.306 37.180
21 30.684 36.794 29.670 36.493
22 30.080 36.058 29.027 35.787
23 29.450 35.294 28.365 35.054
24 28.766 34.513 27.685 34.293
25 28.035 33.711 27.007 33.518
26 27.270 32.890 26.319 32.728
27 26.495 32.052 25.643 31.939
28 25.710 31.201 24.958 31.123
29 24.923 30.341 24.271 30.304
30 24.131 29.477 23.590 29.481
31 23.345 28.606 22.892 28.640
32 22.556 27.735 22.191 27.796
33 21.775 26.862 21.487 26.944
34 21.006 25.989 20.783 26.097
35 20.233 25.112 20.095 25.254
36 19.452 24.240 19.400 24.408
37 18.681 23.370 18.707 23.560
38 17.921 22.504 18.018 22.714
39 17.178 21.641 17.324 21.864
40 16.459 20.782 16.627 21.014
41 15.734 19.928 15.944 20.169
42 15.031 19.081 15.264 19.328
43 14.333 18.242 14.595 18.494
44 13.669 17.410 13.931 17.664
45 13.020 16.588 13.272 16.840
46 12.381 15.775 12.616 16.018
47 11.758 14.974 11.972 15.204
48 11.144 14.185 11.328 14.398
49 10.538 13.409 10.682 13.593
50 9.952 12.646 10.053 12.803
51 9.379 11.898 9.432 12.020
52 8.836 11.167 8.802 11.239
53 8.299 10.459 8.199 10.477
54 7.775 9.772 7.593 9.723
55 7.265 9.107 6.996 8.980
56 6.767 8.456 6.422 8.263
57 6.261 7.829 5.872 7.564
58 5.800 7.236 5.339 6.883
59 5.397 6.678 4.812 6.216
60 5.016 6.153 4.307 5.578
61 4.678 5.672 3.840 4.979
62 4.350 5.225 3.400 4.415
63 4.060 4.815 3.024 3.918
64 3.797 4.420 2.708 3.485
65 3.574 4.061 2.422 3.093
66 3.395 3.741 2.180 2.756
67 3.224 3.445 1.970 2.461
68 3.047 3.162 1.787 2.200
69 2.873 2.886 1.624 1.967
70 2.691 2.621 1.471 1.756
71 2.528 2.401 1.348 1.584
72 2.362 2.196 1.238 1.430
73 2.170 1.999 1.134 1.289
74 2.002 1.829 1.042 1.167
75 1.898 1.672 0.965 1.065
76 1.743 1.533 0.904 0.983
77 1.592 1.449 0.834 0.899
78 1.514 1.339 0.784 0.836
79 1.461 1.274 0.735 0.778
80 1.374 1.172 0.694 0.735
81 1.273 1.046 0.661 0.687
82 1.222 0.993 0.631 0.656
83 1.121 0.912 0.604 0.623
84 0.874 0.755 0.569 0.585
85 0.433 0.355 0.522 0.532

Notes:

The econometric model described by Millimet  et al (2002) and logistic regression equations by gender and education are used to calculate the worklife expectancy estimates.   The model is estimated using matched CPS cohorts from 1992–2000 time period as described in the Millimet et al. (2002) paper.  The logistic equation includes independent variable for age, age squared, race, race by age interaction, race by age interaction squared, marital status, martial status by age, occupation dummies, year and year dummies.  The model is first estimated separately for each gender and education level combination for active persons.  The model is then estimated again for inactive persons.