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.

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 and  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.

Finding: Overall, the worklife expectancy estimated using more recent data from 2000-2013 is shorter then in the earlier time period (1992-2000) data set. This is true for younger worker (18-early 40’s); younger workers from the more recent cohorts have a shorter expected work life then younger workers in the earlier cohorts.  Conversely, while older workers in their 40s and 50s have a slightly longer worklife expectancy in the later time period data set. We are in the process of determining the statistical significance of these differences.

Table 4. Comparsion of Worklife Expectancy for 1992-2000 and 2001-2013 Time Periods
1992-2000 2001-2013
Age Less than High School High School Less than High School High School
18 31.469 38.410 30.569 37.314
19 30.926 37.846 30.128 36.833
20 30.306 37.180 29.603 36.237
21 29.670 36.493 29.021 35.590
22 29.027 35.787 28.419 34.917
23 28.365 35.054 27.809 34.231
24 27.685 34.293 27.205 33.539
25 27.007 33.518 26.588 32.830
26 26.319 32.728 25.964 32.108
27 25.643 31.939 25.357 31.387
28 24.958 31.123 24.736 30.646
29 24.271 30.304 24.110 29.892
30 23.590 29.481 23.491 29.136
31 22.892 28.640 22.866 28.371
32 22.191 27.796 22.237 27.599
33 21.487 26.944 21.606 26.819
34 20.783 26.097 20.970 26.034
35 20.095 25.254 20.327 25.239
36 19.400 24.408 19.685 24.446
37 18.707 23.560 19.039 23.648
38 18.018 22.714 18.392 22.850
39 17.324 21.864 17.737 22.044
40 16.627 21.014 17.085 21.242
41 15.944 20.169 16.421 20.432
42 15.264 19.328 15.764 19.627
43 14.595 18.494 15.110 18.825
44 13.931 17.664 14.456 18.024
45 13.272 16.840 13.798 17.220
46 12.616 16.018 13.154 16.429
47 11.972 15.204 12.520 15.641
48 11.328 14.398 11.886 14.859
49 10.682 13.593 11.259 14.081
50 10.053 12.803 10.642 13.311
51 9.432 12.020 10.030 12.550
52 8.802 11.239 9.429 11.798
53 8.199 10.477 8.843 11.057
54 7.593 9.723 8.270 10.333
55 6.996 8.980 7.709 9.618
56 6.422 8.263 7.152 8.912
57 5.872 7.564 6.618 8.230
58 5.339 6.883 6.095 7.560
59 4.812 6.216 5.587 6.908
60 4.307 5.578 5.097 6.280
61 3.840 4.979 4.624 5.677
62 3.400 4.415 4.181 5.112
63 3.024 3.918 3.782 4.593
64 2.708 3.485 3.428 4.128
65 2.422 3.093 3.109 3.700
66 2.180 2.756 2.819 3.312
67 1.970 2.461 2.556 2.960
68 1.787 2.200 2.323 2.646
69 1.624 1.967 2.102 2.359
70 1.471 1.756 1.905 2.101
71 1.348 1.584 1.728 1.869
72 1.238 1.430 1.577 1.670
73 1.134 1.289 1.427 1.484
74 1.042 1.167 1.296 1.322
75 0.965 1.065 1.184 1.181
76 0.904 0.983 1.077 1.054
77 0.834 0.899 0.980 0.942
78 0.784 0.836 0.894 0.843
79 0.735 0.778 0.807 0.750
80 0.694 0.735 0.675 0.636

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 worklife model iin the left panel of the table is estimated using matched CPS cohorts from 1992–2000 time period as described in the Millimet et al. (2002) paper.   The model on the right panel is estimated using data from 2001-2013.

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.  The educational attainment variables used to estimate our model differ from that of Millimet et al. (2002)   In our model, only individuals whose highest level of attainment is high school are included in the high school category.  Millimet et al (2002) includes individuals with some college in the high school category.