New FLSA Rule due in Q1 2015 will address white collar OT exemptions @SHRM reports

This month’s HR magazine makes some educated guesses regarding the potential changes to the FLSA that can be expected to occur in Q1 2015.  According to SHRM’s sources, the new FLSA rule will:

* Increase the minimum salary threshold in determining FLSA exemption status

*More tightly define the percentage of the person’s work time that needs to engaged in exempt duties to be exempt from FLSA OT – SHRM suggest that the new threshold will be at least 50% similar to California’s OT standards

*Narrow the executive FLSA exemption by modifying or eliminating the primary duty standard.

Wage and hour question of the day: Does #FLSA cover alternative work schedules?

The FLSA does not address flexible or alternative work schedules.  According to the DOL, Alternative work arrangements such as flexible work schedules are a matter of agreement between the employer and the employee (or the employee’s representative).  The DOL has numerous articles and opinion letters concerning alternative and flexible work schedules.  A example of a 2009 DOL opinion letter can be found here.

 

 

 

VA hospital admission wait times across the US in 2014

Abstract: VA hospital admitting waiting times have become a political issue in the U.S., as the number of veteran patients experiencing delays has increased over time. The data show that overall VA admission waiting times are not actually that different from other non-VA hospitals.   The data also show that VA wait time problems with delays tend to be concentrated in a fraction of the main hospitals across the US are worrisome. The data also suggest that while VA hospitals have been adjusting their supply of health services to local conditions (mainly related to the age and economic profile of patients), the influx of a large veteran population requiring treatment in primary care or mental health treatment is creating delays.

A preliminary review of the data by Dwight Steward, Ph.D. and Peter Cleary, Ph.D.  is discussed below.

Extent of the problem

VA hospital waiting times for admission across the US are under fierce discussion. Overall, waiting times are not alarmingly high: out of six million appointments scheduled in May 2014, 96% of patients could visit a doctor within 30 days. That is slightly less than a quarter million of patients had to wait for more than a month to get an appointment at a hospital. Performance is not evenly distributed across hospitals. The wait time problem tends to be concentrated in 15 out of the 141 VA hospitals, where more than eight per cent of patients have to wait more than 30 days (Figure 1).

Waiting times

The major issue for VA patients is getting enrolled into the health system. Waiting times are significantly higher for new patients in primary, specialty or mental health care.  In specialty care, new patients have to wait a full 45 days more than the established patients. The difference is 40 days for primary care, and a bit less (32 days) for mental health care.

A few hospitals have extremely long waiting times. For primary or speciality care, an appointment could take up to 5 months. The worst VA hospital performer in mental health care required a little more than 3 months to schedule an appointment. The bulk of established patients would be treated in 4 to 5 days for primary health or specialty care, and even within as little as 2 days in mental health care. For newly enrolled patients, the situation is more dramatic, as the bulk of patients is getting an appointment in just a bit less than 2 months in primary health or specialty care, and in 38 days for mental health care.

External factors

The data indicate that VA hospital waiting times depend on a number of factors. There is no evidence that waiting times depend much on the total number of appointments taken by patients. Hence, hospitals that are receiving many requests are not more inclined to put patients on longer waiting lists. Instead, it seems that a couple of external factors can explain the length of average waiting times.

Some VA hospitals may be faced with more patients than others due to the composition of the population to which they cater health services. For instance, an older population with few economic resources might be prone to illness, and require more care. US states with many military bases might also impact the number of veterans a hospital might need to prepare care for.

VA hospital waiting times for established patients are significantly lower in states with a larger presence of older and poorer people. VA hospitals in those states appear to be able to cope with the larger demand for health services. States with a larger fraction of the labor force employed in the armed forces also have significantly lower waiting times, suggesting that hospital capacity is better built out in those states with more of a military presences. By contrast, a larger fraction of the population with low education is associated with longer waiting times. The findings are quite similar for all three types of health care.

For newly enrolled VA patients, the external factor that is related to their waiting times is the fraction of veterans on the population.. That is the more veterans there are, the longer waiting times for primary or mental health care new patients. VA hospitals appear to adjust their staffing to population characteristics, but less so to the new influx of veterans requiring treatment.

The STATA programs and data used in this preliminary analysis are accessible from the directorybelow.

http://www.employstats.com/project_data/VA/

STATA code:

This file does the analysis for Elance14
* last update: 14/7/2014 17h00

 
*----------------------------------------------------------------------------------------------------------------
*log close
log using /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/results140714d.log, replace
use "/Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/Elance14.dta", clear
set more off

*----------------------------------------------------------------------------------------------------------------
* DATA

*
* creation of categories
*tabstat vet, stats(mean max min sd variance semean)
summarize appoint
gen dumapp = 0
replace dumapp = 1 if appoint>r(mean)

summarize vet
gen dumvet = 0
replace dumvet = 1 if vet>r(mean)

summarize disab
gen dumdisab = 0
replace dumdisab = 1 if disab>r(mean)

summarize medage
gen dummedage = 0
replace dummedage = 1 if medage>r(mean) 

summarize loweduc
gen dumloweduc = 0
replace dumloweduc = 1 if loweduc>r(mean) 

summarize higheduc
gen dumhigheduc = 0
replace dumhigheduc = 1 if higheduc>r(mean)

summarize white
gen dumwhite = 0
replace dumwhite = 1 if white>r(mean)

summarize foreign
gen dumforeign = 0
replace dumforeign = 1 if foreign>r(mean)

summarize labforce
gen dumlabforce = 0
replace dumlabforce = 1 if labforce>r(mean)

summarize unempl
gen dumunempl = 0
replace dumunempl = 1 if unempl>r(mean)

summarize armed
gen dumarmed = 0
replace dumarmed = 1 if armed>r(mean)

summarize meanwage
gen dummeanwage = 0
replace dummeanwage = 1 if meanwage>r(mean)

summarize ssincome
gen dumssincome = 0
replace dumssincome = 1 if ssincome>r(mean)

summarize poverty
gen dumpoverty = 0
replace dumpoverty = 1 if poverty>r(mean)

summarize age60
gen dumage60 = 0
replace dumage60 = 1 if age60>r(mean)

/*
* statistics of time (overall)
tabstat avgnewp, stats(mean max min sd variance semean)
tabstat avgnews, stats(mean max min sd variance semean)
tabstat avgnewm, stats(mean max min sd variance semean)

tabstat avgestp, stats(mean max min sd variance semean)
tabstat avgests, stats(mean max min sd variance semean)
tabstat avgestm, stats(mean max min sd variance semean)

* t test

ttest avgnewp == avgestp
ttest avgnews == avgests
ttest avgnewm == avgestm

*----------------------------------------------------------------------------------------------------------------
* BASIC STATISTICS

* histogram of average waiting times
foreach nser in avgnewp avgnews avgnewm avgestp avgests avgestm {
histogram `nser', start(0) frac addl mlabsize(minuscule) mlabangle(90) normal
graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/hist_`nser'.pdf, replace
}



* histogram of average waiting times, overlay with fraction of different categories
foreach ser in avgnewp avgnews avgnewm avgestp avgests avgestm {
twoway (histogram appoint, start(0) frac color(blue)) ///
       (histogram `ser', frac start(0) ///
	   fcolor(none) lcolor(black)), legend(order(1 "appoint" 2 "avg" ))
	   graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/H_appoint_`ser'.pdf, replace
}

/*
* histogram of Adjudication Active Time, plot for different categories
graph bar (sum) avgnews , over(appoint) asyvars stack legend(order(1 "AA time with denial=0" 2 "AA time with denial=1" 3 "AA time with denial=2" 4 "AA time with denial=3" 5 "AA time with denial=4"))
graph export /Users/peterclaeys/Documents/FORUM/EMTAN/Files/Elance/Elance8/B_aat1_rfe_denial.pdf, replace
*/

* ANOVA test for difference in mean of aat1 by different characteristics

foreach ser in avgnewp avgnews avgnewm avgestp avgests avgestm {
anova `ser' appoint 
anova `ser' dumvet
anova `ser' dumdisab
anova `ser' dummedage
anova `ser' dumwhite
anova `ser' dumloweduc
anova `ser' dumhigheduc
anova `ser' dumforeign
anova `ser' dumlabforce
anova `ser' dumunempl
anova `ser' dumarmed
anova `ser' dummeanwage
anova `ser' dumssincome
anova `ser' dumpoverty
anova `ser' dumage60
}
*/
*----------------------------------------------------------------------------------------------------------------
* Regression analysis
		
		*** DURATION MODEL
		
		foreach ser in `ser' avgnewp avgnews avgnewm avgestp avgests avgestm {
		di " ----------------------------------------------- " `ser' 
		stset `ser'
		stdes
		
		sts graph
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'.pdf, replace
		sts test dumapp, logrank
		streg appoint vet medage loweduc armed ssincome poverty, dist(exp) nohr
		/*
		sts graph
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'.pdf, replace
		
		sts graph, by(dumvet)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_vet.pdf, replace
		
		sts graph, by(dumdisab)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_disab.pdf, replace
		
		sts graph, by(dummedage)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_medage.pdf, replace
		
		sts graph, by(dumwhite)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_white.pdf, replace
		
		sts graph, by(dumloweduc)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_loweduc.pdf, replace
		
		sts graph, by(dumhigheduc)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_higheduc.pdf, replace
		
		sts graph, by(dumforeign)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_foreign.pdf, replace
		
		sts graph, by(dumlabforce)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_labforce.pdf, replace
		
		sts graph, by(dumunempl)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_unempl.pdf, replace
		
		sts graph, by(dumarmed)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_armed.pdf, replace
		
		sts graph, by(dummeanwage)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_meanwage.pdf, replace
		
		sts graph, by(dumssincome)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_ssincome.pdf, replace
		
		sts graph, by(dumpoverty)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_poverty.pdf, replace
		
		sts graph, by(dumage60)
		graph export /Users/peterclaeys/Documents/FORUM/PROJECTS/2014/Elance14/STS_`ser'_age60.pdf, replace
		
		* test
		sts test dumvet, logrank
		sts test dumdisab, logrank
		sts test dummedage, logrank
		sts test dumwhite, logrank
		sts test dumloweduc, logrank
		sts test dumhigheduc, logrank
		sts test dumforeign, logrank
		sts test dumlabforce, logrank
		sts test dumunempl, logrank
		sts test dumarmed, logrank
		sts test dummeanwage, logrank
		sts test dumssincome, logrank
		sts test dumpoverty, logrank
		sts test dumage60, logrank
		*/
		* regression
		*streg vet disab medage white loweduc higheduc foreign labforce unempl armed meanwage ssincome poverty age60, dist(exp) nohr
		streg vet medage loweduc armed ssincome poverty, dist(exp) nohr
		*streg vet disab, dist(exp) nohr
		}
		/*
		di " %saving estimates in: ...FORUM/PROJECTS/2014/Elance14/results_project_xx0714.xls"
		xml_tab aat01 aat02 aat03 aat04 ///
		using "/Users/peterclaeys/Documents/FORUM/EMTAN/Files/Elance/Elance8/results_project_260514b.xls", tstat below append sheet("results") stats(N r2_a F)
		estimates drop *
		*/
*----------------------------------------------------------------------------------------------------------------
log close

 

Distribution of grants, consulting fees, and other payments to medical professionals by Speciality

The Center for Medical and Medicaid Services (CMS) new Open Payments database shows the consulting fees, research grants, travel and other reimbursements made to medical industry in 2013

There are 2,619,700 payments in the CMS data made to 356,190 physicians.   The average payment made to physicians was $255.22.   The median payment was $15.52.

Overall, Clinical Pharmacology and Orthopedic Surgery professions received the most grants and other payments.  The table below shows the percentage of average payment by specialty as a percentage of the average overall payment to all medical specialties.  The STATA code is listed below.

Medical Specialty % of average payment to all speciallites
Clinical Pharmacology 1516%
Orthopaedic Surgery 473%
Group: Multi-Specialty 436%
Nutritionist 328%
Medical Genetics 284%
Surgery 282%
Transplant Surgery 268%
Neurological Surgery 266%
Pathology 203%
Pediatrics 197%
Oral & Maxillofacial Surgery 160%
Laboratories 154%
Preventive Medicine 150%
Nuclear Medicine 135%
Neuromusculoskeletal Medicine 132%
Phlebology 130%
Radiology 123%
Thoracic Surgery 121%
Colon & Rectal Surgery 121%
Internal Medicine 116%
Anesthesiology 116%
Pharmacy Technician 115%
Dentist 108%
Group: Single Specialty 107%
Otolaryngology 97%
Other Service Providers 91%
Chiropractor 86%
Plastic Surgery 86%
Ophthalmology 84%
Allergy & Immunology 84%
Registered Nurse 83%
Agencies 81%
Technologists, Technicians & Other Technical Service Providers 79%
Physician Assistant 77%
Obstetrics & Gynecology 77%
Dermatology 74%
Podiatrist 73%
Psychiatry & Neurology 70%
Pain Medicine 64%
Urology 64%
Physical Medicine & Rehabilitation 57%
General Acute Care Hospital 57%
Counselor 56%
General Practice 52%
Dental Hygienist 43%
Student, Health Care 43%
Clinical Neuropsychologist 43%
Assistant, Podiatric 41%
Clinic/Center 40%
Optometrist 38%
Military Hospital 37%
Long Term Care Hospital 37%
Emergency Medicine 36%
Personal Emergency Response Attendant 36%
Respiratory, Developmental, Rehabilitative & Restorative Service Providers 35%
Dietary Manager 35%
Family Medicine 33%
Hospitalist 33%
Speech, Language & Hearing Service Providers 32%
Managed Care Organizations 31%
Electrodiagnostic Medicine 31%
Pharmacist 28%
Legal Medicine 27%
Nursing & Custodial Care Facilities 26%
Psychologist 24%
Hospital Units (Psychiatric and Rehabilitation) 22%
Licensed Practical or Vocational Nurse 20%
Suppliers 17%
Psychoanalyst 17%
Special Hospital 16%
Denturist 15%
Dental Laboratory Technician 14%
Social Worker 14%
Dietitian, Registered 13%
Residential Treatment Facilities: Mental Illness, Retardation, and/or Developmental Disabilities 11%
Psychiatric Hospital 10%
Eye & Vision Technician: Technologist 10%
Behavioral Analyst 9%
Emergency Medical Technician 9%
Dental Assistant 9%
Marriage & Family Therapist 9%
Chronic Disease Hospital 8%
Independent Medical Examiner 7%
Nursing Home Administrator 5%
Nurse’s Aide 5%

STATA Code

se “dataopenrecords-small.dta”, clear

rename recipient_state State
drop if State==”” | State==”AE” | State==”AA”| State==”AP” | State==”GU”| State==”ON” | State==”VI”| State==”PR”

keep physician_spec total number
rename p Specialty
sort S total number
drop if S==””
destring number, replace
collapse (mean) tot num , by(S)
rename tot Average_Payment_Amount
rename num Average_Number_of_Payments
outsheet S Average_P Average_N using “P:Business Dev ProjectsEmployStats9074 – OpenRecordsTablesspecialty_payments.csv”, comma nolabel replace

 

Retail Salespersons experienced the largest increase of job openings nationwide for September

Retail Salespersons experienced the largest increase of new openings of all occupations in the US for the month of September with 1,153 new job openings.

retail salesman

Month Occupation Total_Openings New_Openings
Sep-14 Retail Salespersons 52,394 1,153

Source: BLS

Image Source: http://www.businessweek.com/articles/2013-02-04/blackberry-and-best-buy-two-super-bowl-ad-flops

All major MSAs experienced decrease of job openings from August to September

Each major MSA (metropolitan statistical area) in the United States experienced a decrease in the number of total job openings from August 2014 to September 2014. Of all the major MSAs during this span, the New York-Northern New Jersey-Long Island MSA had the largest number of total openings for September, while the Ocean City MSA had the smallest decrease in job openings.

Month MSA Total Openings New Openings
Sep-14 New York-Northern New Jersey-Long Island, NY-NJ-PA 164,748 -32,386
Sep-14 Ocean City, NJ 1,978 -425

Source: BLS

STATA or R for data analysis in wage and hour cases?

In the stats world there is somewhat of a debate going on regarding which statistical analyses programs are “better”.  Of course, the answer always depends on what you use it for.  Some like the open-source, developing nature of R.  While others like the established and tried STATA.

In the world of labor and employment economics and in ligation matters that require data analysis of large sets of data, STATA wins hands down.  However, the open source nature of R is appealing in some settings; but the many decades of pre-written (and de bugged) programs make STATA the best choice in most employment and wage and hour cases that require analysis of large data sets.  Performing basic tabulations and data manipulations in R requires many lines of code while STATA often has the command built in.

Here are some interesting snippets from the web on the R v STATA debate:

http://www.researchgate.net/post/What_is_the_difference_between_SPSS_R_and_STATA_software

The main drawback of R is the learning curve: you need a few weeks just to be able to import data and create a simple plot, and you will not cease learning basic operations (e.g. for plotting) for many years. You will stumble upon weirdest problems all the time because you have missed the comma or because your data frame collapses to a vector if only one row is selected.

However, once you mastered this, you will have the full arsenal of modern cutting-edge statistical techniques at your disposal, along with in-depth manuals, references, specialized packages, graphical interface, a helpful community — and all at no cost. Also, you will be able to do stunning graphics.

 

http://forum.thegradcafe.com/topic/44595-stata-or-r-for-statistics-software/

http://www.econjobrumors.com/topic/r-vs-stata-is-like-a-mercedes-vs-a-bus

 

 

Texas and Eagle Ford Shale rig counts rise while US’ falls from September to October

The U.S. rig count was 1,929 in October 2014, down from 1,931 in September 2014; Texas rig count was 901 in October 2014, up from 896 in September 2014; The Eagle Ford Shale rig count was 211 in October 2014, up from 207 in September 2014.

us_rig_2014_010

texas_rig_2014_010eagle_rig_2014_010

Source:Baker Hughes

Household services for males and females living in Mexico

Everyone knows that people do more than work for pay.  In an injury or death case, it is common for plaintiffs to assert that they have been economically harmed because the  injured or deceased person can no longer provide household services.  Household services include activities such as mowing the lawn, taking care of the household, etc.

Below are some rough tabulations for the number of households services (in hours) performed for males and females residing in Mexico. The data is obtained from the Mexican Time Use Survey.  Generally females spend substantially more time on household production activities.

For males:

inside_hwork 1.27
foodprep 2.95
petandhome 1.58
hhmanagement 1.08
shopping 1.18
obtain_services 0.05
travel_hhactivity 0.05

For females:

 

inside_hwork 12.91
foodprep 14.68
petandhome 1.55
hhmanagement 1.2
shopping 2.12
obtain_services 0.09
travel_hhactivity 0.17