“Big Data” For Jobs And The Economy

According to the McKinsey Global Institute, big data will become “a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.” Here are some key summary points on why:

• Transparency – Big data makes information more transparent and usable.
• Better Management Decisions – Big data improves short- and long-term business and operations planning.
• Segmentation – Big data can be used to create more precisely tailored products and services.
• Improved Decision Making – Big data supplies analytics that can substantially improve decisions.
• Smarter Development – Big data improves the development of the next generation of products and services.

For more on each of these points, please check out the report; it’s worth a read. Each point dovetails neatly with how workforce and economic development groups can use data and analysis to improve and drive local economies.

BIG DATA, JOBS & THE ECONOMY

When it comes to jobs and the economy, the “big data” that often comes to mind is the massive amount of information collected on the US labor market. No other country in the world collects and distributes (i.e., makes public) nearly as much labor market data as the United States. Right now we classify and release data on about 1,100 different industries and 800 occupations.

When organizations such as workforce boards, community colleges, economic development groups, chambers of commerce, utilities, universities, and private groups have this data, they can use it develop clear lines of sight for a wide array of workforce and economic development initiatives. Here is quick rundown of the data that we are referring to. NOTE: There is, of course, more data that we could consider, but these are the building blocks we’d like to start with.

• Industries – Industries are distinct groups defined by similar economic activities (e.g., health care, agriculture, manufacturing, information). According to the Bureau of Labor Statistics, “Workplaces (establishments) are classified into industries based on their principal product or activity, as determined from information on annual sales volume.” Industries are classified according North American Industry Classification System (NAICS).
• Occupations – Occupations are jobs. They are the fundamental building blocks that staff industries. For more on occupations, please see the BLS page on the subject. Occupations are classified according to Standard Occupational Classification (SOC) codes.
• Demographics – Demographics (for our purposes) are the age, gender, and ethnicity of the people who make up a workforce. As data approaches improve, we can track things such as demographics by industry, which is very helpful for more advanced modeling and analytical approaches. Demographic data comes from the US Census Bureau.
• Competencies – Competencies are the key knowledge, skills, and abilities (KSAs) that make up specific occupations. This data, which is survey-based, has grown in importance because it helps us better understand the essential work activities of particular occupations. The primary source of this data is from O*NET.

When we start to combine these datasets and map elements together via staffing patterns and other linkages, we gain a good picture of a regional economy and its requisite parts.

DRAWBACKS OF THE DATA

As with any data, there are limitations. Before we discuss the key things we can do with the data, it is important to understand the common pitfalls.

• Collection – The collection and distribution of so much data presents a key problem: it is nearly impossible for individuals to go to the many sources and analyze economic data on their own.
• Interpretation – Because the data is collected and distributed by the government, some of it is suppressed for privacy reasons. Data can also be relatively difficult to understand if you don’t already have an economics background.
• Timeliness – Data is released at different time intervals. It is important to remember that all data like this therefore has some sort of time lag on it.
• Communication – Many people have a natural intimidation about using labor market data because as soon as they bring it to the public, they will have to explain what it means and where they got, which can be a bit tricky.

NOTE: Companies such as EMSI exist to help clear up these problems.

BENEFITS OF THE DATA

Now, let’s discuss the benefits of labor market data.

• Objectivity – One of the most common things we hear people say is that data helps them be objective. If you are with a public agency or if you’re trying to make a big workforce or economic development decision that will impact a lot of people, you want to make sure it is based on solid research. Gut feeling or simple anecdotes is an okay place to start, but you need to test those notions with a data-driven approach based on labor market analysis and local ground-truthing.
• Persuasive – Data also has the unique ability to convince the skeptics. Your numbers need to support the notion that an economic development or workforce approach will be a good use of public funds and will positively impact local business, jobseekers, or students. If you can show real numbers that support your idea, even dubious people will feel at least a bit more confident.
• Strategic – Data provides workforce and economic development organizations with a strategic advantage. Organizations can use data to make better decisions based on (a) what will have a better impact for the community, (b) what will produce long-term employment, and (c) what will have a solid ROI for public funds. Imagine that there are two educational institutions: one is using a labor market approach to actively validate various workforce education programs, and the other is relying on the gut feeling of a committee at the college. Which one would you rather enroll in? Data will provide organizations like this with a natural competitive advantage, and will produce programs that are far more sustainable in the long run.
• Smarter/More Efficient – Take the same two colleges in our previous example. A data-driven approach will tell planners what program makes the most sense based on the availability of jobs and income. Should the college see an area where there is less demand for labor, they can respond accordingly by scaling down education programs so they are not oversaturating the local market with workers who cannot find work. This is good not only for the bottom line at the college, but also for the students — who should not be over-enrolled in courses where there is not as much labor market demand. The point here is that organizations that are actively using data can operate with much less waste and in a way that benefits its patrons.
• Foundational – Another thing we commonly hear people say is that data is their foundation. It is very often the first step they take when facing any decision. A solid data foundation helps them build stronger systems in a way they can justify to the state, the business community, and jobseekers/students.

PRACTICAL WAYS TO USE DATA

So, having said all this, we now explore some very practical ways to use labor market data.

The first thing we want to discuss is the concept of mapping. When we take these data sources and link them together, we see key inter-relationships that allow for a broader, deeper, and more meaningful level of analysis. Here’s an illustration. Industry data provides a general sense of how a particular manufacturing sector is doing in a specific region, and from this data we learn about trends, supply chain, and income. However, if we use only industries to assess an economy, we probably won’t get the entire picture. We need to drill down and look at the workforce, education, and skills to get a truly three-dimensional local picture. In addition, pure industry data might not make that much of an impact to a lot of people in the community. Until we begin to discuss the actual related jobs, the light bulbs don’t really go on. So we can move from industries to specific occupations — an easy move that can be done by applying a simple staffing pattern. The staffing pattern is a particular data mapping that indicates what jobs or occupations comprise a specific industry. When we have the occupation information mapped to the specific industry, we can see what workers are in demand, what the specific wages are, and what education is required. In addition, further mapping to O*NET helps us understand the specific knowledge, skills, and abilities associated with each occupation. We can also tap into IPEDs data to see what educational programs link up to the occupations.

The mapping we refer to looks something like this:

Industries —> Occupations —> Skills —> Education/Training Programs

This recent Community College Times article illustrates our point. Note: The article is not about labor market information, but it illustrates the importance of mapping the data.

Here is a list of things we can do with labor market data when we have the mapping in place:

1. Make targeted development decisions – Should we go after this or that? What will the impact be? What occupations are we focused on? How much do the jobs pay? This approach is not only great for planning, but it will also give a huge boost and a lot of credibility to communication and marketing.
2. Make better planning decisions – We’ve already made this point, but it’s worth saying again: a data-driven approach to planning always yields more favorable results, and leads to workforce and economic development programs that make a lot more sense for local businesses and jobseekers.
3. Support jobseeker/student success – We cannot stress this point enough. Jobseekers and students have a real thirst for good information about careers and the local economy. They also want to know that you know how to help them find a good career. As a result, workforce and education organizations are finding it necessary to show greater accountability and reporting. Data should be the starting point for workforce and economic development initiatives aimed at helping people find their way into a better career.
4. Create focus that the organization can support – This point echoes what we said earlier about objectivity and establishing a strong foundation. Many public agencies tasked with supporting workforce and economic development efforts seemed pulled in different directions. This, of course, creates a lack of focus; it’s hard to get things done. Data will be a great asset here because administrators can use it to explain and justify a particular approach that gets everyone on the same page. If organizations stay focused and everyone has the same priorities about which problems need to be addressed, then chances are the organization will be more successful.
5. Gain broader support - Similarly, if one organization (like a workforce board) can show other organizations (like colleges, economic developers, or chambers) that it is important to prioritize a specific industry sector through the use of data, it could have huge impacts for the region. Why? Because a community-wide initiative with organizations working toward common goals functions a lot like a military unit trying to win a battle. Businesses and jobseekers benefit from the coordinated efforts of public agencies that fight for the same goals. The ticket here is data: it helps us pick out the right targets, and it is key to determining the best strategy.
6. Understand community demand for education and training – We’ve already discussed the concept of mapping. If a particular educational program is offered, and yet students have a hard time finding jobs, then a quick program review should be conducted to see if the program is actually calibrated to a specific occupational need. This process is simple if we just start at the program level, trace to target occupations, look at the wages and trends, and then discover what industries are employing these occupations. If the data isn’t favorable, the college should consider changing the program so that it lines up to a true labor need.
7. Pursue dollars for sustainable programs – Many public organizations spend a lot of time pursuing grant and foundation dollars. An excellent way to demonstrate to the foundation or grant committee that you could use the money is to show data that tells a story and indicates there will be a real ROI. You can also prove that the funds will be applied to an area that has real, long-lasting potential.

CONCLUSION

Public workforce and economic development groups need to embrace big data. There is a wealth of information that they can take advantage of, and the benefits are enormous. Companies such as EMSI exist to help, and have designed software and consulting services to help you tap into big data. If you have questions or comments, or if you would like to learn more about how to foray into labor market data and analysis, please contact us.

Contact Rob Sentz (rob@economicmodeling.com) and follow us on Twitter to stay up to date: @DesktopEcon