Four New Categories of EMSI Data
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This document discusses changes made to employment data categorization and modeling by EconomicModeling.com. It aims to provide a better breakdown of employment data by separating workers into four new categories - 1) QCEW employees, 2) non-QCEW employees, 3) self-employed workers, and 4) extended proprietors. This allows users to reproduce state and Bureau of Labor Statistics figures more accurately. Definitions of the new categories are provided. The document also outlines how users can leverage the different categories and data sets to analyze trends in employee versus self-employed work and total proprietor activity.
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Four New Categories of EMSI Data
- 2. Whe re w e ’ re he ade d Why? W W Definitions W Uses W Questions W WWW.ECONOMICMODELING.COM
- 3. Why? WWW.ECONOMICMODELING.COM
- 4. W h y W e ’ v e D o n e It • Better breakout of the data • Allow users to reproduce state and BLS figures • More flexibility • Better understanding of worker categories WWW.ECONOMICMODELING.COM
- 5. Definitions WWW.ECONOMICMODELING.COM
- 6. Tw o G roups • E m p lo y e e s - Wage and salary workers - Includes commission, tips, etc. • P r o p r ie t o r s - Self-employed in unincorporated business WWW.ECONOMICMODELING.COM
- 7. 4 New Categories Slide for old data framework
- 8. Slide for old data framework
- 9. E m p lo y e e s 1. Q C E W - Unsuppressed QCEW - Exact match with published numbers 2 . N o n -Q C E W E m p lo y e e s - - Employees outside of QCEW - Rail workers, military, etc. WWW.ECONOMICMODELING.COM
- 10. P r o p r ie t o r s 3 . S e lf -E m p lo y e d - Self-employment is primary income 4 . E x t e n d e d P r o p r ie t o r s - Self-employment is peripheral income - Finance & insurance, real estate, etc. WWW.ECONOMICMODELING.COM
- 15. N a t io n a l T r e n d s
- 16. N a t io n a l T r e n d s
- 17. Uses WWW.ECONOMICMODELING.COM
- 18. How to Use WWW.ECONOMICMODELING.COM
- 19. S et 1 QCEW Match State and BLS WWW.ECONOMICMODELING.COM
- 20. S e ts 1 & QCEW, 2 non-QCEW Employee vs. Self- Employed WWW.ECONOMICMODELING.COM
- 21. S e t s 1, 2 , 3 QCEW, non-QCEW, Self-Employed Employment minus extended proprietors WWW.ECONOMICMODELING.COM
- 22. S e t s 1, 2 , QCEW, non-QCEW, 3 , 4 Self-Employed, Extended Proprietors Complete Picture WWW.ECONOMICMODELING.COM
- 23. S et 3 Self-Employed Primarily self-employed WWW.ECONOMICMODELING.COM
- 24. S et 3 , 4 Self-Employed, Extended Proprietors Total proprietor activity WWW.ECONOMICMODELING.COM
- 25. How to Use WWW.ECONOMICMODELING.COM
- 26. Whe re w e ’ ve been Why? W W Definitions W Uses W Questions W WWW.ECONOMICMODELING.COM
- 27. Q U E S T IO N S ? T y p e in t o t h e b o x o n yo u r s c r e e n C a ll 2 0 8 . 8 8 3 . 3 5 0 0 E m a il in f o @ c o n o m ic m o d e lin g . c o m e WWW.ECONOMICMODELING.COM
Editor's Notes
- Welcome, everyone! We’re going to talk about some exciting new changes to EMSI’s data, but before we do, a couple housekeeping details. First, you’ll be hearing three voices on this call. This is Josh Stevenson, Analyst Product Manager. This is Andrew Crapuchettes, CEO of EMSI. And this is Deacon James, VP of Data Products. Because of the hearty, masculine quality of my voice, I’ll be leading the webinar, and Andrew and Deacon will be correcting my errors and making the whole thing more compelling and interesting.[MORE] We’ve muted all participants’ lines, so you won’t have to worry about making noise on your end. We do want you to ask questions, so you’ll have to do that through the handy chat feature in the Adobe Connect application. We’ll handle those at the end, and try to get to all of them. Adobe Connect also has a “full-screen” feature, so you’ll want to find that to get the best possible display.[MORE] We’re recording this series of webinars and will post one of them. The information we’re covering here has also been covered in a blog post, and we’re currently working on producing a short video or two in order to help everyone get completely up to speed.[MORE] Also, I want to mention that we see some definite applications for the data. But we’ve learned that our users always come to the data from different, interesting perspectives. As you use the data, and even as you view the webinar, please let us know if you come up with a way of using these sets that strikes you as particularly cool. We’d love to know about it. Let’s get started.
- We also want to say that we’re excited about this change. It’s been in the works for a long time, and we think it’s going to be a huge leap forward for everyone who uses EMSI data. Here’s what we’re going to cover today: We’ll talk about why this change is useful, why it’s happening. Then we’ll define some of the new terminologies we’ll use to talk about data. Then, because this new system will allow us to choose from a number of different data perspectives, we’ll talk about why you’d use combination X in one situation and combination Y in another. Then we’ll leave time to answer your questions.
- So, under they WHY? heading: Before we talk about methodology or anything, we want to show you why what we’re doing is exciting, and give you a picture of what you’ll be able to do with these new data categories. To do that we’ll head over to the tool. [The following will be printed out and followed as we walk through the tool.]
- I hope you agree with us that that is a pretty cool thing to be able to do. But getting a better break out of the data is just one reason why we’re making this change. We have a number of clients, maybe some of you, who have really excellent reasons why they need to stay as close to state and BLS figures as possible. Our covered set has stayed pretty close, but now when you’re looking at our QCEW category in the data, if there’s an employment number published in QCEW it will match the number you’ll see in Analyst. There are some qualifications on that. Since we created this set, 2010 QCEW has been revised slightly, and fourth quarter 2011 QCEW has been released, so we are not going to match QCEW dead on at the moment. However, once the next dataset is released (a month or so), we’ll match. Dead on, even. The new data will give you opportunities to be much more flexible. Switching between employee and proprietor sets allows you to approach data questions with incredible nuance. And this is also an opportunity for you to get to understand these worker categories. If you work with EMSI data you’re probably a bit of a data geek. This will make you even data geekier. It’s a foregone conclusion that this is a good thing, right? Understanding these worker categories lets you weigh in on a slew of new questions and serve the basic purpose of labor market data: make better decisions based on data.
- Now we’ll move under the Definitions heading.
- First, our datasets are primarily concerned with those workers who are classified as either “employees” or “proprietors.” Employees are often referred to as simply “wage and salary workers.” This includes workers receiving wages and salaries, as well as those working for commission, tips, pay-in-kind, and other similar forms of payment. Broadly speaking, any worker who does not fall into the “employee” category will be considered a proprietor. These are people who work for their own unincorporated business, practice, or farm. It is important to note that people who work for their own incorporated businesses are considered wage and salary workers for their own companies, and are thus not considered proprietors. Okay, the stage is almost set for us to define our four new categories. But first . . .
- . . . we should talk a little about the way our previous data set worked. As we mentioned at the beginning you used to be able to see either covered workers or all workers. So you can see here that you had COMPLETE which was made up of covered and non-covered. You couldn’t get to the non-covered dataset. It was just everything that we added to COVERED to make COMPLETE. This means that there were some distinct pieces of data living inside of the covered and non-covered sets that you didn’t have access to.
- This how the data is arranged in the new scheme. We have totals and then we have employees and proprietors. Under that we have QCEW and non-QCEW categories under Employees and self employed and extended proprietors under Proprietors. This bottom row represents the four categories. QCEW, non-QCEW, Self-employed, and Extended Proprietors.
- Now let’s talk about what these four categories mean. First, the Employees: QCEW This is simply unsuppressed QCEW and will closely resemble what you’d get from your state LMI shop. This means that any employment number that QCEW publishes will show up in our tool as the exact same number. NON-QCEW This set is other groups who are not captured by QCEW, but who still count as employees. This includes railroad, military, some non-QCEW federal government workers, UI-exempt non-profits, and a few other miscellaneous categories.
- Self-Employed - These are self-employed workers who count their self-employed work as their primary source of income. Extended Proprietors - These are workers who are counted as proprietors, but classify the income as peripheral to their primary employment. Many industries (primarily oil & gas extraction, finance & insurance, and real estate) include people who are considered sole proprietors or part of a partnership, yet have little or no involvement or income in the venture. So, let’s take a look at how this works out in a particular industry sector.
- We’re looking at the total breakdown of the REAL ESTATE sector in terms of these four new data categories. The purple and green represent the proprietors and the red and blue represent employees.
- In our past data sets you’d be able to see either just the red and blue as covered . . .
- . . . or all four categories lumped in together, as complete.
- The advantage of our new approach is that we can look at each of these categories individually or mix and match them in whatever grouping we like. Previously we would not have been able to articulate the proprietor category accurately. Not to mention the fact that we would not have been able to make a distinction between extended proprietors and the self-employed category. This shows us a very interesting perspective on Real Estate as an industry sector. The vast majority of workers in real estate are in that extended proprietor category. That means that almost everyone working in real estate doesn’t claim it as their primary source of income. This seems like a really helpful thing to be able to see, doesn’t it?
- We can also use this data to look at much bigger trends. For this table we’ve included the percentage of total employment each category has accounted for nationally from 2001-2012. The first thing to notice is how the proportion of workers captured by QCEW has declined. While it is still the largest area of employment, the portion covered as payroll employees (think big companies with benefit packages) has declined. To be clear the ratio of QCEW workers has declined, due almost entirely to the fact that extended proprietors have grown by 50% over that period. Again, extended proprietor employment represents a lot of 1099 workers who do things like have a hobby business, invest in real estate, or even referee sports. The proportion of self-employed and non-QCEW workers has remained fairly stable over the past 11 years. Again, extended proprietor employment represents a lot of 1099 workers who do things like have a hobby business, invest in real estate, or even referee sports. The proportion of self-employed and non-QCEW workers has remained fairly stable over the past 11 years.
- This chart shows what the four datasets look like by industry for each 2-digit NAICS sector. Sectors like manufacturing and admin, support and waste management are dominated by QCEW workers. Sectors like education services, other services, and government have larger numbers of non-QCEW workers. Extended proprietors make up roughly half or more of the agriculture, mining, and real estate industries, as we saw previously. Many self-employed workers can be found in agriculture, construction, and the arts.
- Now we’ll move on to USES.
- We’ve created this handy sheet to help you determine when you need to mix and match the various data categories. It’ll be available on the blog. The way we see it there are six really useful options:
- If you want to match state of BLS data, this set will work the best for you.
- If you want to primarily consider jobs that people would apply for, instead of jobs dominated by self-employment, you’ll use these two sets together.
- If you want to know what workers in your region are doing, excluding peripheral employment, these three sets will make sense.
- If you want to know everything that’s happening in your region, you’ll add in all four sets.
- If you need a picture of all workers who are primarily self-employed, you’ll look at three on its own.
- If you need a picture of all of the proprietor activity in the region, you’ll use both three and four.
- Once again, that’s all covered on this sheet, so that’ll be a good thing to keep on hand as you explore this new data set.
- Okay. Let’s briefly run over what we’ve covered. We talked about why we’ve made the changes. Basically you’re going to be able to answer more nuanced questions and investigate deeper with EMSI data than you ever could before. We talked about the distinction between employee and proprietor data, and then defined the four different sets: QCEW, non-QCEW employment, Self-Employed, and Extended Proprietors. Then we discussed the various uses you might have for mixing and matching the data sets. The take away is this: now EMSI data can give you even deeper insight into any region, and answer a ton of new questions. And this is all part of our mission to make the best labor market data available and easy to use.
- That’s all we have. Now we’re going to see if we’ve received any questions via chat and we’ll let Andrew and Deacon handle those. [After questions] Well, it looks like that’s everything. Thanks very much for your attention here. We hope you’re all excited for what you’ll be able to do with these four data categories, and we hope it helps equip you to do even better work, making more informed decisions, building stronger economies, with Labor Market Data. Thanks!