Median house prices for all three MSAs (metropolitan statistical areas) fell from December 2014 to January 2014.
Image source: http://www.asemooni.com/news/economic-news/the-president-agreed-with-the-increase-in-housing
CPI falls faster than medical care commodities while medical services rise from Dec to Jan
The consumer price index (CPI) went down from 236.284 in December 2014 to 234.677 in January 2015, an annualized rate of 8.16%.
The price index for medical care commodities went down at an annualized rate of 3.37% from December 2014 to January 2015. During the same period, the price index increased for medical care services (0.98%), hospital and related services (0.59%), and professional services (2.09%).
Source: BLS
Image source: http://www.shutterstock.com/pic-54762670/stock-photo-background-concept-illustration-consumer-price-index.html
Natural gas prices decrease while crude oil prices rise in February
WTI crude oil price increased from $47.79 per barrel in January 2015 to $53.56 per barrel mid February 2015. Natural gas price fell from $2.88 per million BTU (one million BTU is approximately 974 cubic feet) in January 2015 to $2.75 per million BTU mid February 2015.
Texas crude oil production for December 2014 was 67,660,682 barrels, down from 74,910,932 barrels reported in November 2014. Texas natural gas production was 600,389,620 Mcf (thousand cubic feet) of gas in December 2014, down from the November 2014 gas production total of 675,593,444 Mcf.
Sources: eia.gov, rrc.state.tx.us
Worklife expectancy for U.S. workers – updating the Millimet et al. (2002) econometric model of worklife
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.
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.
Our approach is two fold. First we matched the BLS data cohorts based on the Millimet et al. (2002) and Peracchi and Welch (1995) papers. In a nutshell the CPS matching routine involves matching incoming and outgoing cohorts across a given year. Once the data is matched, we then look at the work status of the individuals to determine if they were active or in active across the year that they were interviewed by the BLS.
Using this matched data we next replicated the work of Millimet et al. (2002) using the 1992 to 2000 CPS data as they did in their paper. In general the Millimet et al. (2002) econometric model uses a standard logistic regression framework to estimate transitional probabilities based on a two state labor market framework where a person is either active or in active in the workforce.
The methodology begins by estimating logistic regression using individuals who were active when first interviewed. Independent variables such as the occupation, gender, marital status and number of children are included in the logistic regression. A separate regression is estimated for individuals who are inactive at the start of the BLS interview. Separate active and inactive regressions are also estimated for certain factors of interest, such as education attainment level and reported disability status.
The logistic regression equations provide the probabilities that are conditional on the labor force attachment of the individual at the time of the interview. The conditional probabilities yield the transitional probabilities for initially active or in active individuals. For example, a person who is active at the start of a period could be either active or inactive in the next period. The transitional probabilities obtained from the logistic regression is used to calculate the probability that a person who is active at the start of a period could be either active or inactive in the next period in this example.
As described in the Millimet et al (2002) paper, the expected work life for each age is obtained recursively by working backwards from an assumed terminal year (T+ 1). The terminal year is the year in which after no one is assumed to be active. In the analysis a terminal age of 80 or 85 is used.
Using the model we began by replicating the Millimet et al. (2002) econometric model. After we replicated the model, we then performed some additional work and expanded logistic regression worklife equations. The results of our estimation are shown in the tables that are attached.
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 during our replication. 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.
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.
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.
Texas RN’s, therapists, and PA’s see increase in job openings from Nov to Dec
The number of job openings in Texas for nurses, therapists, and physician assistants increased from 9,189 to 10,144 from November 2014 to December 2014. The searcher-to-job opening ratio decreased from 0.85 to 0.63 during that same span.
Source: BLS
Image source: http://www.carrollhs.org/s/1253/index.aspx?pgid=877
FLSA OT report for individuals working in grounds maintenance occupations
In this post, we look at the weekly overtime (OT) hours typically worked by those who work in grounds maintenance 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: |
| Grounds Maintenance Workers | 15.85% | 11.2 hours | 53.5 hours |
U.S. BLS data inddicates that approximately 15.85% of grounds maintenance workers work overtime hours in a given week. On average, these workers that have FLSA overtime work approximately 11.2 hours a week in OT. The average regular or straight time pay rate of theise workers in the U.S. is approximately 11.90 an hour. The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay is 16.35 an hour.
Source: BLS (CPS March)
Texas healthcare jobs increased by 0.3% from Nov to Dec
The health care and social assistance industry gained 4,300 jobs from November 2014 to December 2014. Compared to December 2013, the cumulative number of jobs added in this industry is 57,000, an annual increase of 4.3%.
http://www.tracer2.com/admin/uploadedPublications/2127_TLMR-January_15.pdf
Image source: http://blogs.wsj.com/health/2012/01/06/health-care-sector-adds-jobs-as-overall-employment-picture-looks-healthier/
Texas oil and gas extraction jobs increased by 0.5% from Nov to Dec
The oil and gas extraction industry in Texas gained 600 jobs from November 2014 to December 2014. Compared to December 2013, the cumulative number of jobs added in this industry is 5,600, an increase of 5.2%.
Source: http://www.tracer2.com/admin/uploadedPublications/2127_TLMR-January_15.pdf
Image Source: http://www.eliteexploration.com/texas-oil-gas-companies/
Texas oil and gas extraction jobs increased by 0.5% from Nov to Dec
The oil and gas extraction industry in Texas gained 600 jobs from November 2014 to December 2014. Compared to December 2013, the cumulative number of jobs added in this industry is 5,600, an increase of 5.2%.
Source: http://www.tracer2.com/admin/uploadedPublications/2127_TLMR-January_15.pdf
Image Source: http://www.eliteexploration.com/texas-oil-gas-companies/
Texas hydro-fracturing job openings decreased from Nov to Dec
The number of job openings in Texas for “petroleum engineers” and “geological and petroleum technicians” decreased from 632 in November 2014 to 595 in December 2014, while the searcher-to-job opening ratio also decreased from 0.89 to 0.78 in the same span.
The number of job openings in Texas for “derrick operators” and “roustabouts” decreased from 271 in November 2014 to 259 in December 2014, while the searcher-to-job opening ratio also decreased from 11.14 to 10.35 in the same span.
Source: BLS
Image source: http://wonderfulengineering.com/what-is-petroleum-engineering/