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 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.
California experienced a decrease of 4,200 innovation job openings from December 2014 to January 2015, a decrease of 18.09%. Texas experienced an increase of 1,345 innovation job openings from December 2014 to January 2015, an increase of 11.19%.
| State | Total_Openings | Monthly_Change | Yearly_Change |
| CA | 19,018 | -18.09% | -6.77% |
| TX | 13,366 | 11.19% | 26.83% |
Innovation jobs definition: http://www.employstats.com/blog/2014/09/26/1233/
Source: BLS
Image source: http://www.bizjournals.com/sacramento/news/2013/09/23/symposium-innovation-ecosystems-jobs-wea.html
California experienced a decrease of 10,856 innovation job openings from December 2014 to January 2015, a decrease of 16.18%. Texas experienced an increase of 4,472 innovation job openings from December 2014 to January 2015, an increase of 11.30%.
Our definition of STEM jobs: http://www.employstats.com/blog/2014/09/19/growing-national-interest-in-stem-fields-has-focused-our-research/
| State | Total Openings | Monthly Change | Yearly Change |
| CA | 56,235 | -16.18% | -1.70% |
| TX | 44,042 | 11.30% | 28.05% |
Source: BLS
Image source: http://projecttomorrowblog.blogspot.com/2013/11/i-am-scientist.html
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)
All four largest MSAs (metropolitan statistical areas) in Texas experienced an increase in job openings from December 2014 January 2015.
Dallas
Dallas experienced an increase of 24,720 job openings from December 2014 to January 2015, a 37.22% increase.
| Date | Total Openings | Monthly Change | Yearly Change |
| Jan-15 | 91,133 | 37.22% | 28.50% |
| Dec-14 | 66,413 | -4.20% | -11.31% |
| Nov-14 | 69,326 | -10.74% | 11.28% |
| Oct-14 | 77,670 | 8.74% | 19.49% |
| Sep-14 | 71,430 | -20.43% | -6.57% |
| Aug-14 | 89,770 | -0.90% | 27.29% |
| Jul-14 | 90,586 | 6.86% | 28.67% |
| Jun-14 | 84,771 | 0.29% | 15.23% |
| May-14 | 84,526 | -5.64% | 20.19% |
| Apr-14 | 89,581 | 23.25% | 26.62% |
| Mar-14 | 72,682 | 2.48% | -2.52% |
| Feb-14 | 70,922 | -5.29% | 1.27% |
Austin
Austin experienced an increase of 7,912 job openings from December 2014 to January 2015, a 25.56% increase.
| Date | Total Openings | Monthly Change | Yearly Change |
| Jan-15 | 38,869 | 25.56% | 27.67% |
| Dec-14 | 30,957 | -1.84% | -2.14% |
| Nov-14 | 31,539 | -9.03% | 18.96% |
| Oct-14 | 34,670 | 7.17% | 24.53% |
| Sep-14 | 32,352 | -15.59% | -0.45% |
| Aug-14 | 38,327 | -1.57% | 28.78% |
| Jul-14 | 38,938 | 5.96% | 28.50% |
| Jun-14 | 36,748 | 1.44% | 15.98% |
| May-14 | 36,228 | -4.59% | 19.27% |
| Apr-14 | 37,971 | 22.48% | 25.01% |
| Mar-14 | 31,001 | 1.83% | -3.11% |
| Feb-14 | 30,443 | -3.77% | 0.38% |
Houston
Houston experienced an increase of 20,424 job openings from December 2014 to January 2015, a 34.07% increase.
| Date | Total Openings | Monthly Change | Yearly Change |
| Jan-15 | 80,369 | 34.07% | 26.32% |
| Dec-14 | 59,945 | -3.04% | -10.92% |
| Nov-14 | 61,824 | -11.15% | 10.35% |
| Oct-14 | 69,582 | 8.73% | 19.68% |
| Sep-14 | 63,998 | -20.30% | -5.99% |
| Aug-14 | 80,299 | -1.83% | 27.86% |
| Jul-14 | 81,792 | 8.29% | 29.66% |
| Jun-14 | 75,527 | -0.09% | 14.68% |
| May-14 | 75,596 | -5.17% | 20.16% |
| Apr-14 | 79,716 | 20.94% | 26.09% |
| Mar-14 | 65,912 | 3.59% | -1.16% |
| Feb-14 | 63,626 | -5.45% | 2.27% |
San Antonio
San Antonio experienced an increase of 9,886 job openings from December 2014 to January 2015, a 37.06% increase.
| Date | Total Openings | Monthly Change | Yearly Change |
| Jan-15 | 36,562 | 37.06% | 29.10% |
| Dec-14 | 26,676 | -3.61% | -10.95% |
| Nov-14 | 27,676 | -11.80% | 12.69% |
| Oct-14 | 31,377 | 8.93% | 21.21% |
| Sep-14 | 28,805 | -20.44% | -4.98% |
| Aug-14 | 36,207 | 0.12% | 27.65% |
| Jul-14 | 36,164 | 6.00% | 27.85% |
| Jun-14 | 34,117 | 0.26% | 15.48% |
| May-14 | 34,029 | -5.52% | 20.28% |
| Apr-14 | 36,017 | 23.93% | 26.62% |
| Mar-14 | 29,061 | 2.62% | -2.85% |
| Feb-14 | 28,320 | -5.47% | 0.33% |
Source: 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.
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.
California
California experienced a decrease of 11,258 job openings from November 2014 to December 2014, a 6.91% monthly decrease.
| Month | Total Openings | Monthly Change | Yearly Change |
| Dec-14 | 436019 | -2.52% | -6.91% |
| Nov-14 | 447277 | -10.45% | 15.15% |
| Oct-14 | 499494 | 7.47% | 21.56% |
| Sep-14 | 464759 | -18.01% | -2.91% |
| Aug-14 | 566834 | -0.67% | 27.78% |
| Jul-14 | 570648 | 6.01% | 27.68% |
| Jun-14 | 538310 | 0.55% | 16.09% |
| May-14 | 535368 | -5.24% | 20.85% |
| Apr-14 | 565001 | 23.20% | 26.68% |
| Mar-14 | 458591 | 2.14% | -2.59% |
| Feb-14 | 448997 | -4.13% | 0.79% |
| Jan-14 | 468361 | 20.57% | 11.64% |
Texas
Texas experienced a decrease of 8,140 job openings from November 2014 to December 2014, a 2.92% monthly decrease.
| Month | Total Openings | Monthly Change | Yearly Change |
| Dec-14 | 270,849 | -2.92% | -10.76% |
| Nov-14 | 278,989 | -11.11% | 10.78% |
| Oct-14 | 313,873 | 8.34% | 19.17% |
| Sep-14 | 289,703 | -20.46% | -5.86% |
| Aug-14 | 364,236 | -1.23% | 27.59% |
| Jul-14 | 368,784 | 7.36% | 28.75% |
| Jun-14 | 343,517 | 0.21% | 14.99% |
| May-14 | 342,800 | -5.20% | 20.03% |
| Apr-14 | 361,597 | 22.06% | 25.80% |
| Mar-14 | 296,241 | 3.08% | -1.95% |
| Feb-14 | 287,386 | -5.31% | 1.30% |
| Jan-14 | 303,498 | 20.51% | 12.47% |
Source: BLS
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
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.