Dwight Steward, Ph.D. – Economic Thinking In Action

Perhaps one of the most useful STATA commands out there…

Working with wage and hour data and employment data, like we do on a daily basis involves the analysis of very large data sets. Big data and employment data are often one in the same. STATA has a very useful command that allows you to load in large Excel 2007/2010 spreadsheet files. It is:

set excelxlsxlargefile on

This simple command allows the user to bypass the pre-set limit on spreadsheet size.

Just remember, STATA and your computer will be unresponsive during the load. So be patient and let it all load up.

Calculating business interruption damages of a cattle operation

There a number of different types of economic damages that arise in business interruption cases. One type of damage is the lost profits associated with the business interruption. Another type has to do with the loss of asset and/or property values. Out-of-pocket expenses associated with the incident that caused the business interruption is another common loss in business litigation.

The example below involves business interruption litigation involving a midsize cattle operation. The impacted cattle operation is is based on an 1000 acre ranch in Central Texas. The cattle company earned its revenue from the sell of registered cows and calves, commercial steers, hay and other agricultural products related to cattle production.
The company’s the ranch operation was damaged by a welding fire that was sparked in the main section of the property. The fire caused a number of buildings to be damaged. Several livestock areas suffered damage that resulted in the death of livestock and the damage and destruction of machinery and equipment.
The company had business interruption insurance. The company’s insurance policy worked off a formula that reimbursed the company for a set period of lost profits, out-of-pocket expenses, and expenses to get the business back to where it was prior to the incident.

After the fire the ranch management filed a claim with the insurance company. The insurance company did their analysis and reimbursed them for their losses according to the policy. The ranch owners did not agree with the amount that was paid by the insurance company. The ranch owners calculated that they were both out of pocket a lot more than the insurance company calculated and their lost profits were higher. The ranch also calculated that it would take a longer period of time for them to be able to get back up to speed.
Ultimately after several year months of dealing with insurance company the ranch owners suit the insurance company and other parties including the company that started the fire. In this case the ranch alleges that several types of economic loss associated with the business interruption have occurred.

The ranch first alleges incurred out-of-pocket us expenses associated with the cleanup of the damage and destruction of its property and assets. In this case these expenses include removing machinery replacing machinery and disposing of livestock. In addition the company has also incurred a loss associated with the amount that will be required to get the company back to where it was before the fire. In this instance, these expenses include fixing and replacing the machinery fixing and replacing buildings and purchasing new livestock. In addition to the out-of-pocket expenses associated with the remediation of the damage and the purchase of new equipment and livestock, the business operation has also alleges lost profits during in the time period in which they were the remediation occurred. In this instance the company lost out on calf sales, hay, and other agricultural products that were damaged or destroyed from the fire. The ranch operation also was not able to conduct its auction of its crop because of the fire.

Big data question: How big of a random sample is big enough in a wage in hour case?

That’s a question that comes up a lot in wage and hour land employment lawsuits. Typically the question is how many employees do I need to look at to have a statistically significant sample?

In some instances it’s not feasible to collect data or get all the records for
all the employees of a particular company. Sometimes the data is kept
in such a way that it takes a lot of effort to get that information. In
other instances it is a matter of the limitations of imposed by the court.

In any event, that’s a question that comes up a number times in wage and hour lawsuits particularly ones involving class or collective actions. So what’s the answer?

Generally, the size of the sample needs to be sufficiently large so that it is representative of
the entire employee population. That number could be relatively small say 40 employees or relatively large say to 200 employees depending on the number of employees at the company and the characteristics of the employee universe that is being analyzed.

For example if there are no meaningful distinctions between the employees in the universe, that is
it is generally accepted that all the employees are pretty much all
similarly situated, then a sheer simple random sample could be
appropriate.

That is, you could simply draw names from a hat, essentially. A simple random sample typically requires the smallest number of employees.

If there are distinctions between employees that need to be accounted for, then
either a larger sample or some type of stratified sampling could be appropriate.
Even if there are distinctions between employees, if the sample is sufficiently large then distinctions between the employees in the data could take care of themselves.

For instance, assume that you have a population of 10,000 employees and they are
divided into four different groups that need to be looked at differently.

One way to do a sample in this setting is to sample over each of the different groups of employees separately. The main purpose of the individual samples is to make sure that you have the appropriate number of employees in each of the different groups. That is, to make sure that the number of employees in the different samples are sufficiently representative of the distribution of the different groups of employees in the overall population.

Another way to do this is to simply just take a large enough sample so that the distinctions take care of themselves. If the sample is sufficiently large then the distribution of the different groups of employees in the sample should on be representative of the employee population as a whole.

So in this example, if there is a sufficiently large sample it could be okay to use a simple random sample and you would get to the same point as a more advanced stratified type of approach.

The key however is to make sure that the sample is sufficiently large that of course depends on the overall population and the number of groups of employees being studied.

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

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

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 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.

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.

Comparsion of CPS matched data sets – Millmet et al (2002) to Steward and Gaylor (2015)

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. . 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.

Table 1. Comparsion of CPS cohort matched data sets
Year	Millimet et al. (2002)	Steward and Gaylor (2015)
1992/93	37,709	36,652
1994/95	34,418	33,377
1996/97	31,691	32,739
1997/98	32,276	32,972
1998/99	32,083	32,893
1999/2000	32,739	33,164
Total	200,916	201,797

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.

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.

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

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

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