Tag: bls

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.

 

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.

The data for all years is shown below.  Ultimately there were over 590,000 data points used in the analysis.

Table 2.  Matched CPS Sample Sizes 1993-2013
	Female				Male
Year	Less than High School	High School	Some College	College	Less than High School	High School	Some College	College	Total
1993	3,766	7,326	4,898	3,452	3,376	5,619	4,280	3,935	36,652
1994	3,539	7,019	5,357	3,619	3,097	5,477	4,411	4,013	36,532
1995	3,082	6,161	5,086	3,545	2,664	4,815	4,086	3,938	33,377
1997	3,079	6,172	4,771	3,488	2,723	4,857	3,926	3,723	32,739
1998	2,839	6,113	4,873	3,672	2,694	4,952	3,995	3,834	32,972
1999	2,709	6,027	4,987	3,770	2,513	4,830	4,134	3,923	32,893
2000	2,692	5,930	5,009	3,915	2,463	4,899	4,052	4,204	33,164
2001	2,545	5,806	4,971	3,901	2,458	4,919	4,232	4,016	32,848
2003	1,096	3,218	2,579	2,411	1,019	2,701	2,122	2,470	17,616
2004	2,579	6,372	5,803	5,009	2,394	5,307	4,745	4,819	37,028
2005	2,039	5,378	5,146	4,673	1,867	4,632	4,270	4,285	32,290
2006	2,297	5,500	5,608	4,657	2,131	4,953	4,263	4,389	33,798
2007	2,147	5,730	5,466	5,060	2,076	5,133	4,344	4,592	34,548
2008	2,159	5,659	5,787	5,281	2,040	5,212	4,593	4,826	35,557
2009	2,027	5,637	5,780	5,556	2,023	5,062	4,776	4,976	35,837
2011	1,845	4,844	5,106	5,136	1,786	4,603	4,176	4,432	31,928
2012	1,733	4,849	4,930	4,956	1,779	4,693	4,151	4,616	31,707
2013	1,658	4,542	5,061	5,109	1,668	4,579	4,271	4,650	31,538
Total	43,831	102,283	91,218	77,210	40,771	87,243	74,827	75,641	593,024

Notes:

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.