U.S. Economy – Page 4 – Economic Thinking In Action

STATA statistical code for estimation of Millimet et al. (2002) econometric worklife model

The STATA code for estimating the Millimet et a;. (2002) econometric worklife model can be found below. The code will need to be adjusted to fit your purposes. However, the basic portions are here.

use 1992-2013, clear

drop if A_W==0
keep if A_A>=16 & A_A<86

*drop if A_MJO==0
*drop if A_MJO==14 | A_MJO==15

gen curr_wkstate = A_W>1
lab var curr_wkstate “1= active in current period”
gen prev_wkstate = prev_W>1
lab var prev_wkstate “1= active in previous period”
gen age = A_A
gen age2 = age*age
gen married = A_MA<4
gen white = A_R==1
gen male = A_SE==1

gen mang_occ = A_MJO<3
gen tech_occ = A_MJO>2 & A_MJO<7
gen serv_occ = A_MJO>6 & A_MJO<9
gen oper_occ = A_MJO>8

gen occlevel = 0
replace occlevel = 1 if mang_occ==1
replace occlevel = 2 if tech_occ==1
replace occlevel = 3 if serv_occ==1
replace occlevel = 4 if oper_occ ==1

gen lessHS = A_HGA<=38
gen HS = A_HGA==39
gen Coll = A_HGA>42
gen someColl = A_HGA>39 & A_HGA<43

gen white_age = white*age
gen white_age2 = white*age2
gen married_age = married*age

gen child_age = HH5T*age

/*
gen mang_occ_age = mang_occ*age
gen tech_occ_age = tech_occ*age
gen serv_occ_age = serv_occ*age
gen oper_occ_age = oper_occ*age
*/

merge m:1 age using mortalityrates

keep if _m==3
drop _m

gen edlevel = 1*lessHS + 2*HS + 3*someColl + 4*Coll

save anbasemodel, replace
*/ Active to Active and Active to Inactive probabilities

local g = 0
local e = 1

forvalues g = 0/1 {

forvalues e = 1/4 {

use anbasemodel, clear

xi: logit curr_wkstate age age2 white white_age white_age2 married married_age HH5T i.year_out if prev_wk==1 & male==`g’ & HS==1
*Gives you conditional probability
*summing these figures gives the average predicted probabilities

predict AAprob

keep if occlevel==`e’
*collapse (mean) AAprob mortality, by(age)

collapse (mean) AAprob mortality (rawsum) MARS [aweight=MARS], by(age)

gen AIprob = 1-AAprob

replace AAprob = AAprob*(1-mortality)
replace AIprob = AIprob*(1-mortality)

save Active_probs, replace

*Calculates Inactive first period probabiliteis

use anbasemodel, clear

xi: logit curr_wkstate age age2 white white_age white_age2 married married_age HH5T i.year_out if prev_wk==0 & male==`g’ & HS==1

predict IAprob

keep if occlevel==`e’

*collapse (mean) IAprob mortality , by(age)
collapse (mean) IAprob mortality (rawsum) MARS [aweight=MARS], by(age)

gen IIprob = 1-IAprob
save Inactive_probs, replace

*Calculates WLE for Active and Inactive

merge 1:1 age using Active_probs

drop _m

order AAprob AIprob IAprob IIprob
*Set the probablilties for end period T+1

*Note the top age changes to 80 in the later data sets
gen WLE_Active = 0
replace WLE_Active = AAprob[_n-1]*(1+AAprob) + AIprob[_n-1]*(0.5 + IAprob)
gen WLE_Inactive = 0
replace WLE_Inactive = IAprob[_n-1]*(0.5+AAprob) + IIprob[_n-1]*IAprob

gen WLE_Active_2 = 0
replace WLE_Active_2 = WLE_Active if age==85

gen WLE_Inactive_2 = 0
replace WLE_Inactive_2 = WLE_Inactive if age==85
local x = 1
local y = 80 – `x’

forvalues x = 1/63 {

replace WLE_Active_2 = AAprob*(1+WLE_Active_2[_n+1]) + AIprob*(0.5 + WLE_Inactive_2[_n+1]) if age==`y’
replace WLE_Inactive_2 = IAprob*(0.5 + WLE_Active_2[_n+1]) + IIprob*WLE_Inactive_2[_n+1] if age==`y’

local x = `x’ + 1
local y = 80 – `x’

}

keep age WLE_Active_2 WLE_Inactive_2
rename WLE_Active_2 WLE_Active_`g’_`e’
rename WLE_Inactive_2 WLE_Inactive_`g’_`e’

save WLE_`g’_`e’, replace

keep age WLE_Active_`g’_`e’
save WLE_Active_`g’_`e’, replace

use WLE_`g’_`e’, clear
keep age WLE_Inactive_`g’_`e’
save WLE_Inactive_`g’_`e’, replace

di `e’
/**End of Active to Active and Active to Inactive probabilities*/

local e = `e’ + 1
}

local g = `g’ + 1

}
local g = 0
local e = 1

forvalues g = 0/1 {

forvalues e = 1/4{

if `e’ == 1 {
use WLE_Active_`g’_`e’, clear
save WLE_Active_`g’_AllOccLevels, replace

use WLE_Inactive_`g’_`e’, clear
save WLE_Inactive_`g’_AllOccLevels, replace

}

if `e’ > 1 {

use WLE_Active_`g’_AllOccLevels, replace
merge 1:1 age using WLE_Active_`g’_`e’
drop _m
save WLE_Active_`g’_AllOccLevels, replace

use WLE_Inactive_`g’_AllOccLevels, replace
merge 1:1 age using WLE_Inactive_`g’_`e’
drop _m
save WLE_Inactive_`g’_AllOccLevels, replace

}

local e = `e’ + 1
}

if `g’ ==1 {
use WLE_Active_0_AllOccLevels, clear
merge 1:1 age using WLE_Active_1_AllOccLevels
drop _m
save WLE_Active_BothGenders_AllOccLevels, replace
use WLE_Inactive_0_AllOccLevels, clear
merge 1:1 age using WLE_Inactive_1_AllOccLevels
drop _m
save WLE_Inactive_BothGenders_AllOccLevels, replace
}

local g = `g’ + 1

}

!del anbasemodel.dta

A narrative description of the Millimet et. al (2002) econometric worklife model

The following describes the approach used by Millimet et al (2002) to estimate U.S. worker worklife expectancy. The pdf version can be found here: Millimet (2002) Methodology Description

Methodology

First, transition probabilities are obtained from a two state labor market econometric model. The two labor market states are active and inactive in the workforce. The transition probabilities are the probabilities of going from one labor market state to another, such as active in one period and inactive in the next period. There are four such transition probabilities (Active-Active, Active-Inactive, Inactive-Active, Inactive-Inactive). The transition probabilities are obtained from the conditional probabilities estimated using a standard logit frame work. The logit model states:

Where y is equal to 1 if the individual is active and y equals 0 if the individual is inactive in the workforce during the period. Logit regression models are estimated separately for active and inactive individuals. For example, for a person who is initially active, the two estimated transition probabilities (Active to Active and Active to Inactive) equations are:

The estimated transition probabilities for persons who are initially inactive are estimated in a similar manner. The transition probabilities/conditional probabilities are used to construct predicted transition probabilities for each individual in the data set.

The average of the individual predicted probabilities for each age are ultimately used to calculate the transition probabilities in the Millimet et al. (2002) econometric worklife model. The average predicted transition probabilities at each age are:

In the calculation the averages are weighted by the CPS weights. Also anine year moving average is used to smooth out the transition probabilities.

The worklife expectancy at each age can be determined recursively. Specifically, if there is an assumed terminal year (T+1) in which no one is in the workforce, then the worklife expectancy for each age prior can be determined by working backwards in the probability tree. For instance at the terminal year, the individual’s worklife in the terminal year is the worklife probability in that terminal year. For example, assume that after age 80 no individuals are active in the work force. In this example, the probability that a person who is active at age 79 will be active at age 80, is the worklife expectancy for the individual at age 79. As described below this fact allows the worklife for all ages to be determined recursively using the transition probabilities obtained from the logistic regression models.

So specifically, the worklife () is the probability that the person active at time T remains active at the beginning of period T+1 (or end of T). It is assumed that no one is active after time period T+1. Similarly, the worklife () is the probability that the person inactive at time T is active at the beginning of period T+1 (or the end of T). Accordingly, there are multiples ways that a person at the end of time period T-1 can arrive at being active or inactive at the end of T, the terminal year. For instance, the person could be active in T-1 and then active in T. The transition probability for the is person is: . Alternately the person could be inactive in T-1 and active in T. The transition probability for this person is Two similar transition probabilities can be obtained for persons who are initially inactive at time T-1.

Using the worklife expectancies( and ) for the year prior to the terminal year can be calculated using the four transition probabilities described above. Specifically the worklife expectancies are as follows.

The 0.5 factor is included to account for the assumption that all transitions are assumed to occur at mid year.

Using this methodology, the worklife expectancy for each year prior to the terminal year in a recursively fashion.

Texas saw greater increase in job openings than US and Cali for Jan

Texas both experienced a greater increase in job openings than California and US for the month of January.

January 2015

State	Total Openings	Monthly Change	Yearly Change
California	425,877	-2.33%	-5.15%
Texas	366,165	35.19%	27.41%
USA	4,393,597	27.61%	22.75%

Source: BLS

Big BLS employment data, disability, and worklife expectancy

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. In addition we also wanted to supplement and expand on a few additional topics. The additional topics included looking at different definitions of educational attainment, adding in reported disability, and looking at occupational effects on worklife expectancy.

Finding: We also looked at the worklife expectancy for individuals with and without a reported disability. Disability was not covered in the Millimet et al. (2002) paper. As has been well reported, the disability measure in the BLS data is very general in nature. Accordingly the applicability of the BLS disability measure to litigation is somewhat limited. However it is interesting to note that there is a substantial reduction in worklife expectancy exhibited by individuals who reported have a disability. On average the difference is about 10 years of work life. This is consistent with other studies on disability that a relied on the BLS data. Other factors such as occupation and geographical region do not appear to have much impact on WLE estimates.

FLSA OT report for individuals working in Derrick, rotary drill, and services unit operators, oil, gas, and mining occupations

In this post, we look at the weekly overtime (OT) hours typically worked by those who work in Derrick, rotary drill, and services unit operators, oil, gas, and mining occupations.

Many of the employees that work in these jobs are not exempt from FLSA overtime pay and earn 1.5 times pay for hours worked over 40 in a given week.

The tabulations below are based on U.S. BLS survey data. The BLS job title groups are insightful, generally containing more specific job titles with similar knowledge, skills, and abilities (KSA), but can be more broad than a particular company’s job title listing. Also, some companies may have the job title listed here as exempt from FLSA or state OT due to their specific job assignments. The BLS does not make a distinction as to if the job title is exempt or non-exempt from OT.

Occupational Group Title	Percent of OT Workers	Average Hours of OT	1 Out of Every 4 (25%) OT Workers Works at Least
Derrick, rotary drill, and services unit operators, oil, gas, and mining	66.67%	25.6	80

U.S. BLS data indicates that approximately 66.67% of Derrick, rotary drill, and services unit operators, oil, gas, and mining workers work overtime hours in a given week. On average, these workers that have FLSA overtime work approximately 25.6 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately 32.10 an hour. The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay is 48.14 an hour.

Source: BLS (CPS March)

Younger workers today have slightly less attachment to the workforce than younger workers in the past

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 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.

Elementary and Middle School Teachers experienced the largest increase of job openings nationwide for Dec

Elementary and Middle School teachers experienced the largest increase of new openings of all occupations in the US for the month of December with 4,017 new job openings.

Month	Occupation	Total_Openings	New_Openings
Dec 2014	Elementary and Middle School Teachers	34,298	4,017

Source: BLS

Replication of the Millimet et al. (2002) work was sufficient and yielded similar results

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.

Tallahassee, FL experienced largest increase in job openings of all US MSAs for Dec

The Tallahassee, FL MSA (metropolitan statistical area) experienced the largest increase of job openings of all MSAs in the United States for the month of December with 155 new openings.

Month	MSA	Total Openings	New Openings
December 2014	Tallahassee, FL	8,330	155

Source: BLS

FLSA OT report for individuals working in roofing occupations

In this post, we look at the weekly overtime (OT) hours typically worked by those who work in roofing occupations.

Many of the employees that work in these jobs are not exempt from FLSA overtime pay and earn 1.5 times pay for hours worked over 40 in a given week.

Occupational Group Title	Percent of OT Workers	Average Hours of OT	1 Out of Every 4 (25%) OT Workers Works at Least
Roofers	23.64%	13.3 hours	50 hours

U.S. BLS data indicates that approximately 23.64% of roofers work overtime hours in a given week. On average, these workers that have FLSA overtime work approximately 13.3 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately 15.23 an hour. The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay is 22.84 an hour.

Source: BLS (CPS March)