Good day to you all
This is my last blog. It's been really fun being in D.C. and I'll miss it, but I'll also be happy to come back home, especially to see my dog.
Today will be the last installment of the absolutely riveting Employment and Salary series. We'll discuss the MPH, specifically the fields of Epidemiology and Healthcare Administration. So, without any more dilly-dallying, let's get started.
There are 5,100 epidemiologists in the country, according to the BLS, with 500 jobs being added every ten years. This is a growth rate about average for the job market. Because there isn't a single epidemiology degree, it's hard to chart how you'll fare when you graduate.
According to Salary.com, the epidemiologist salary spread is fairly tight, with a median of $84,493, a 25th percentile of $74,344, and a 75th percentile of $94,087. This is far below the other salaries we've talked about, but the tight spread means you at least know what to expect.
Next, the Heathcare Administrator
Medical and Health Services management is, right now, growing incredibly rapidly, with a growth rate of 23% between now and 2024. There are 315,000 jobs total with 73,500 expected to be added in the next ten years.
According to Salary.com, Clinical Operations Manager (an example of healthcare administration which involves running clinical operations at a hospital), make a median salary of ~$120,000. The spread is quite a bit wider, with a middle 50% range from $103,000 to $146,000
That's all about that I have to say. Look forward (or don't) to the full presentation, coming sometime in may,
Wednesday, April 23, 2014
Tuesday, April 22, 2014
Employment and Salary: MBA
Hey
This is my final week here in D.C., and by extension, my final week of blogs. After tomorrow's final blog, I will cease updating this and instead begin finalizing a first draft of my Senior Research project presentation.
Today, I'll do for MBA's what I did last week for patent attorneys, and what I'll do tomorrow for MPH's--I'll look at employment numbers and salary distribution
I can not, unfortunately, mind many absolute numbers for MBA's and it's nearly impossible to find anything specific to science majors. But, for business majors employment seems to be very good. In the U.S.A, LinkedIn reports a 95% employment rate after graduation for people who hold MBA's, which is up from just 91% just a year prior. This is a lot better than the baseline rate for recent college graduates (92.1%) and way above the after-graduation rate for Ph.D.'s, which sits at a measly 65%. Of course, these rates climb as time goes on, with 96% of college graduates and 98.5% of Ph.D.'s eventually finding work, but solid work right after graduation is generally a good thing and a fantastic first step to a prosperous life.
Salarywise, the picture looks good. Here is an article from Bloomberg about salaries based on field and institution. What's most striking is how variable it all is. It really is dependent on what business school you choose and what field you go to work in, BUT the overall market looks to be fairly high-paying, with median salaries usually in the >$100,000 range
This is my final week here in D.C., and by extension, my final week of blogs. After tomorrow's final blog, I will cease updating this and instead begin finalizing a first draft of my Senior Research project presentation.
Today, I'll do for MBA's what I did last week for patent attorneys, and what I'll do tomorrow for MPH's--I'll look at employment numbers and salary distribution
I can not, unfortunately, mind many absolute numbers for MBA's and it's nearly impossible to find anything specific to science majors. But, for business majors employment seems to be very good. In the U.S.A, LinkedIn reports a 95% employment rate after graduation for people who hold MBA's, which is up from just 91% just a year prior. This is a lot better than the baseline rate for recent college graduates (92.1%) and way above the after-graduation rate for Ph.D.'s, which sits at a measly 65%. Of course, these rates climb as time goes on, with 96% of college graduates and 98.5% of Ph.D.'s eventually finding work, but solid work right after graduation is generally a good thing and a fantastic first step to a prosperous life.
Salarywise, the picture looks good. Here is an article from Bloomberg about salaries based on field and institution. What's most striking is how variable it all is. It really is dependent on what business school you choose and what field you go to work in, BUT the overall market looks to be fairly high-paying, with median salaries usually in the >$100,000 range
Thursday, April 17, 2014
Employment and Salary: Patent Attorney
Hello all
In my last three blogs, I will be looking at employment statistics of the kind i've described over the last few posts for the three main fields I've been studying. As before, I'm going to start off today with patent law, which is the most likely field a scientist who gets a law degree would go into.
First up in our statistics is absolute number of jobs. According to the U.S. Patent Office, there are 41,800 patent attorneys on register. This is a vast over-estimate, as many of those registered are dead or retired. This patent law blog puts the number of active attorneys at a much lower figure of around 26,000, or possibly fewer. Compare this to the total number of lawyers, which the Bureau of Labor and Statistics puts at around 759,800, and you see just how relatively small a field IP law is and why patent and IP attorneys are in such high demand. In fact, since not all types of IP lawyers require a science degree, the actual number of those active attorneys WITH a science degree is even lower. This means that it's potentially quite easy to land a job as a patent attorney.
Next up is absolute job growth. For the overall field of law, the BLS puts the number of jobs added between 2012 and 2022 at ~74,800, with a field growth rate of around average speed.. However, over 40,000 law jobs are added per year, meaning that by 2022 there will be a massive surplus of law students and a massive shortage of jobs. As for patent law itself, I can't find many statistics, but the general impression I get is that growth is slow but not as slow as law in general.
Finally, salary breakdown
I couldn't find the exact sort of breakdown I described in the last post. But, I found a website which compiles estimates from many different firms. The best one of these estimates was from Salary.com and it is reproduced here
Next time I'll do the same thing for business
In my last three blogs, I will be looking at employment statistics of the kind i've described over the last few posts for the three main fields I've been studying. As before, I'm going to start off today with patent law, which is the most likely field a scientist who gets a law degree would go into.
First up in our statistics is absolute number of jobs. According to the U.S. Patent Office, there are 41,800 patent attorneys on register. This is a vast over-estimate, as many of those registered are dead or retired. This patent law blog puts the number of active attorneys at a much lower figure of around 26,000, or possibly fewer. Compare this to the total number of lawyers, which the Bureau of Labor and Statistics puts at around 759,800, and you see just how relatively small a field IP law is and why patent and IP attorneys are in such high demand. In fact, since not all types of IP lawyers require a science degree, the actual number of those active attorneys WITH a science degree is even lower. This means that it's potentially quite easy to land a job as a patent attorney.
Next up is absolute job growth. For the overall field of law, the BLS puts the number of jobs added between 2012 and 2022 at ~74,800, with a field growth rate of around average speed.. However, over 40,000 law jobs are added per year, meaning that by 2022 there will be a massive surplus of law students and a massive shortage of jobs. As for patent law itself, I can't find many statistics, but the general impression I get is that growth is slow but not as slow as law in general.
Finally, salary breakdown
I couldn't find the exact sort of breakdown I described in the last post. But, I found a website which compiles estimates from many different firms. The best one of these estimates was from Salary.com and it is reproduced here
- Patent Attorney I (JD + 0-2 years of experience) – $80,683 (average); low 41K and high 128K
- Patent Attorney II (JD + 2-5 years of experience) – $123,276 (average); low 88K and high 179K.
- Patent Attorney III (JD + 5-8 years of experience) – $146,262 (average); low 115K and high 195K.
- Patent Attorney IV (JD + 8 years or more of experience) – $170,971 (average); low 140K and high 214K.
- Top Patent Attorney (JD + 15 or more years of experience) – $210,437 (average); low 145K and high 270K.
- Patent Agent II (Bachelors + 2-5 years of experience) – $84,509 (average); low 62K and high 118K.
Next time I'll do the same thing for business
Wednesday, April 16, 2014
Salary Statistics
Hey guys
This is my second-to-last week here in D.C. and my fourth-to-last blog. I'll be sad to leave the D.C. area but I'll be equally happy to come back home.
Today I'm going to talk about salary in the general sense, rather than a field-by-field sense. I'll talk about the different ways salary is reported and their uses.
So when you look for a job one of the first things you look for is the salary statistics. You want to know how much you're going to make! But, they can be misleading. There are two main ways salary is reported--Average Salary and Median Salary.
Averages take the sum of all salaries in the field and divide it by the number of people employed. In datasets with low variance, averages are a good way to look at...well...an average of a bunch of different things. But, averages are sensitive to extremes. If three people in a field are making $50,000 a year, and one person is making $200,000, the average salary is $87,500. This would give job-seekers the impression that they would make, on average, that much, when that is patently untrue.
Medians work differently. Median is the exact middle value of a set of data. In a dataset with large variance, like the one previously mentioned, medians tend to be more along the lines of what one could expect to find. Because medians are dependent only on the number of points, they're far less sensitive to extremes.
For a more complete picture of the salary field, you'd use a quartile system. You'd report the salaries of the bottom 25%, the middle 50% and the top 25%. You could use median or average in this. This shows the most accurate picture of the distribution of salaries in a field and provides someone with the best estimate of what they could expect to make.
This is my second-to-last week here in D.C. and my fourth-to-last blog. I'll be sad to leave the D.C. area but I'll be equally happy to come back home.
Today I'm going to talk about salary in the general sense, rather than a field-by-field sense. I'll talk about the different ways salary is reported and their uses.
So when you look for a job one of the first things you look for is the salary statistics. You want to know how much you're going to make! But, they can be misleading. There are two main ways salary is reported--Average Salary and Median Salary.
Averages take the sum of all salaries in the field and divide it by the number of people employed. In datasets with low variance, averages are a good way to look at...well...an average of a bunch of different things. But, averages are sensitive to extremes. If three people in a field are making $50,000 a year, and one person is making $200,000, the average salary is $87,500. This would give job-seekers the impression that they would make, on average, that much, when that is patently untrue.
Medians work differently. Median is the exact middle value of a set of data. In a dataset with large variance, like the one previously mentioned, medians tend to be more along the lines of what one could expect to find. Because medians are dependent only on the number of points, they're far less sensitive to extremes.
For a more complete picture of the salary field, you'd use a quartile system. You'd report the salaries of the bottom 25%, the middle 50% and the top 25%. You could use median or average in this. This shows the most accurate picture of the distribution of salaries in a field and provides someone with the best estimate of what they could expect to make.
Friday, April 11, 2014
FASEB Meeting
Hey guys.
This doesn't have a huge amount to do with my actual research project, but it's related to the internship and it was a highly interesting happening.
I've had an interest in science policy for quite a while, so I've been trying explore that avenue of interest a bit while I'm here. Last week, I met with the APS's science policy coordinator and discussed a bit about what it means to be involved in science policy. It was very interesting to me and I expressed an interest in learning more. Today, I attended a meeting of FASEB.
FASEB (Federation of American Societies of Experimental Biology) is a federation of various biological scientific societies, coordinating and publishing several tens of journals in nearly every area of biological science. A large part of the research they do is what's known as "basic" research, meaning research that doesn't have an immediate clinical or medical value. Rather, it's research that's done with less care for practical applications. The APS is one of the founding societies of FASEB.
So, today I sat in on a meeting of all of the various science policy coordinators of FASEB's constituent societies. They discussed issues like funding for basic research, congressmen who signed onto promises which were then broken, distribution of fact sheets to legislators, and other such things. It was a little bit over an hour in length and provided a lot of valuable insight into the workings of a science policy unit.
Next time, I'll get back on topic
This doesn't have a huge amount to do with my actual research project, but it's related to the internship and it was a highly interesting happening.
I've had an interest in science policy for quite a while, so I've been trying explore that avenue of interest a bit while I'm here. Last week, I met with the APS's science policy coordinator and discussed a bit about what it means to be involved in science policy. It was very interesting to me and I expressed an interest in learning more. Today, I attended a meeting of FASEB.
FASEB (Federation of American Societies of Experimental Biology) is a federation of various biological scientific societies, coordinating and publishing several tens of journals in nearly every area of biological science. A large part of the research they do is what's known as "basic" research, meaning research that doesn't have an immediate clinical or medical value. Rather, it's research that's done with less care for practical applications. The APS is one of the founding societies of FASEB.
So, today I sat in on a meeting of all of the various science policy coordinators of FASEB's constituent societies. They discussed issues like funding for basic research, congressmen who signed onto promises which were then broken, distribution of fact sheets to legislators, and other such things. It was a little bit over an hour in length and provided a lot of valuable insight into the workings of a science policy unit.
Next time, I'll get back on topic
Wednesday, April 9, 2014
Employment and Job Statistics: Part 2--Only a Sith Deals in Absolutes
Hello all,
Last time, I talked about how a simple unemployment rate is often not useful in the overall job market and even less so for specific fields. This was because the unemployment rate is not a perfect picture of the economy and because it's hard to pin down what exactly the labor force IS for any field, making it next to impossible to derive an actual rate.
Today, I'll talk about a slightly more useful statistic--absolute numbers.
There are two important numbers. There's the absolute number of jobs available in the job market and the number of jobs added per annum. How can these numbers be useful? They can be compared to numbers and used as the raw materials for more complicated calculations.
For example, what if you wanted to figure out, like I am, how the job market for lawyers is receiving new graduates. You could try and find employment rates post-graduation for law school graduates from all the various institutes and try to put them together for a meaningful number, or you could take the absolute numbers for graduates and compare that to the absolute numbers of jobs added in a year and compare them. If the number of jobs added a year is far lower than the number of new law school graduates, you would know that something's and that maybe attending law school wasn't worth it. If it's higher, you know that there's a very high demand for lawyers and that jobs are plentiful.
Next time I'll talk about salary by percentile and its use.
Tuesday, April 8, 2014
Employment and Job Statistics: Part 1
After that brief interlude yesterday, today I'm going to be jumping right into the next large area for analysis: employment numbers and hiring statistics. These are just as, if not more important than all of the fields of analysis previously explored. After all, what do hours, salary, and job satisfaction matter if you can't find a job in the first place.
There are a few sub-categories in this field. There's the employment rate, the job growth statistics, the absolute number of jobs available, the absolute job growth, and (since I didn't do it in the pay section), salary distribution. This week, I'll talk about all of these statistics, what they mean, and how they are useful. Next week, I'll begin looking at numbers for specific fields.
The employment rate is a national number put out by the Bureau of Labor and Statistics. It's a description of the number of people working compared to the total number of people in the labor force. It's a general indicator of the state of the economy. It's not the end-all, be-all of employment statistics, though, and is very limited in its use. It doesn't take into account people who have stopped seeking work, or who are employed only partly through the year. Still, it can be useful. The trouble is that this is a national statistic and compiling a field-specific one is more difficult. This is simply because there is no reliable way to count the total labor force for that field, as people are capable of switching careers. An out-of-work mechanical engineer could give up looking for a mechanical engineering job and search instead for an aerospace job. It's very hard to gauge whether someone belongs to one labor force or another.
As such, the employment rate is useful for populations of all sizes, but only as long as they contain the entire population and its workforce, rather than a specific subset.
There are a few sub-categories in this field. There's the employment rate, the job growth statistics, the absolute number of jobs available, the absolute job growth, and (since I didn't do it in the pay section), salary distribution. This week, I'll talk about all of these statistics, what they mean, and how they are useful. Next week, I'll begin looking at numbers for specific fields.
The employment rate is a national number put out by the Bureau of Labor and Statistics. It's a description of the number of people working compared to the total number of people in the labor force. It's a general indicator of the state of the economy. It's not the end-all, be-all of employment statistics, though, and is very limited in its use. It doesn't take into account people who have stopped seeking work, or who are employed only partly through the year. Still, it can be useful. The trouble is that this is a national statistic and compiling a field-specific one is more difficult. This is simply because there is no reliable way to count the total labor force for that field, as people are capable of switching careers. An out-of-work mechanical engineer could give up looking for a mechanical engineering job and search instead for an aerospace job. It's very hard to gauge whether someone belongs to one labor force or another.
As such, the employment rate is useful for populations of all sizes, but only as long as they contain the entire population and its workforce, rather than a specific subset.
Subscribe to:
Posts (Atom)