This is Part 1 of a three-part series that examines how to find and leverage local labor data during the site selection process to help generate solutions during a labor shortage.
The U.S. economy has been navigating an unprecedented expansionary period for well over a year now, which, generally speaking, would normally be considered good news. But if you have any kind of vested interest in workforce development, you’re probably facing some serious challenges and unsettling concerns at this point. Certainly, the outlook for business investment and job creation has rarely been brighter. However, companies should be prepared for the short- and long-term implications of their site selection decisions if they want to take advantage of the opportunities the current economic landscape presents.
When considering that nearly every type of facility search will have a detailed labor component, it’s important to understand how critically workforce development will affect your site selection searches. The demand for skilled labor is a major problem across the U.S., and employers continue to need higher skilled workers while workforce development departments struggle to create strategies aligned to the changing needs of these employers.1
One indicator of the growing concern over available labor can be found in Area Development’s 2017 Corporate Survey, where Availability of Skilled Labor and Labor Costs ranked 2nd and 3rd respectively as major concerns. Just three years prior in their 2014 Corporate Survey, they ranked 5th and 6th respectively.
Like Amazon in its search for HQ2, our clients are looking to locate near communities with pockets of critical skills and which are natural attractors to the elusive 20- to 35-year old cohort. Trying to re-train or relocate an existing labor force is expensive and time consuming.
Finding Quality Data: Leave No Stone Unturned
Analyzing the right type and vintage of data can be a major differentiator between successful and unsuccessful site selection. When searching for new locations, you should cast a wide net and use a process that mimics a funnel. Start with broad parameters and data sets and narrow your focus on more discrete variables as you zero in on a few communities.
Start your process by analyzing your needs. “Know thyself” is key. Define your labor force needs using Standard Occupational Classifications (“SOCs”). The SOCs are publicly available through Bureau of Labor Statistics (“BLS”) and you can research BLS’s Occupational Employment Statistics (“OES”) by using the codes with increasing levels of granularity with two- to six-digit descriptions. To develop a feel for the SOCs relevant to your company, you can search the BLS database for your industry using The North American Industry Classification System (“NAICS”).3
Next, use the SOCs to search the OES data to find states with large concentrations of the occupations that you are seeking. The BLS has maps depicting OES data by employment, location quotient and average wages by state.4 In addition to analyzing the current data, you can research historic information and calculate how the employment, location quotient and wages have changed over time. The OES data is available by SOCs starting in 2010.5
For your top states, drill down into regional, county and Metro Statistical Area (“MSA”) details.6 BLS’s maps mentioned above have data by MSA.7 For regional level data, you can search BLS’s Query System to find OES data at the non-Metro level.7 You also can use the Query System for a variety of searches including one occupation over multiple geographies which is valuable to compare similar data across all of your short-listed locations.
Once you have gathered occupation data for your short-listed sites, you will need to assess the supply, demand, and skills of those occupations. Using public data available through BLS’s Geographic Profile of Employment and Unemployment, you can assess the labor force in your short-listed locations by several demographic factors including: education attainment, age, employment status and other factors.8 Information regarding education attainment for a region is helpful in assessing the skills of the local workforce, while employment status will indicate the amount of potential slack in a community’s labor shed.
To help you refine the analysis, you should research and compare the Location Quotient (“LQ”) for each community. Location Quotient is a measure of a region’s industrial specialization and may be analyzed by several economic factors with employment being one.9 By combining OES and LQ data, you can gain a sense of whether the local community has a specialized knowledge of a particular industry. You can find LQ employment data on BLS.10
Since publicly available data has some drawbacks, you should try to obtain more current data for your specific locations. A good source of data to assess labor supply and demand is the jobs postings for each location. To research job postings, you can go on line and manually down load the information or, a more efficient method is to purchase the data from third-party data sources (e.g., Jobs EQ or EMSI).11 To gauge the labor supply in an area, it is helpful to know how long it takes to fill the position. The faster that a position is filled may indicate that there is abundant labor in the area or that labor is relatively mobile.12 For demand, it is helpful to analyze the number of job postings in the region relative to the national average. If the regional postings were higher than the national average, it may indicate that demand is higher in the community.13 Comparing the labor supply with the employer demand is helpful to know whether the wages could rise or remain stable with the addition of your project.14
When analyzing the data, be careful of its limitations. Typically, publicly available data (e.g., U.S. Census, BLS, American Fact Finder) are free; however, there can be lag times between collection, analysis, and publication. Private sources of data are generally more expensive to access but tend to be more current. In some instances, private providers of data use proprietary algorithms to analyze the data that they are collecting. Before relying on the data, you should know that the data is coming from an algorithm rather than raw data so you can ask about the methodologies used in the analysis and whether those methodologies are consistent with your site selection assumptions.
In addition, you should check the source of the data for the date that it was collected, analyzed, and published. If you receive the data from a state or local community, you should request the source so you can analyze the underlying statistics (e.g., sample size, standard deviation, confidence levels and standard error). Data that is out-of-date and lacking substance reflects poorly on economic development organizations and is an indication of whether they will be able to serve your business effectively after locating in their community.
For help with your site selection analysis or insights into your workforce, contact the Duff & Phelps Site Selection and Incentives Advisory team.
1For a discussion of work force training, please see Martha Laboissiere and Mona Mourshed, “Closing the skills gap: creating workforce-development programs that work for everyone”, McKinsey & Company (February 2017).
2According to the Bureau of Labor Statistics website, “The 2018 Standard Occupational Classification (SOC) system is a federal statistical standard used by federal agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of 867 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are combined to form 459 broad occupations, 98 minor groups, and 23 major groups. Detailed occupations in the SOC with similar job duties, and in some cases skills, education, and/or training, are grouped together.” https://www.bls.gov/soc/ For a list of Standard Occupational Classifications, please see Bureau of Labor Statistics, Occupational Employment Statistics, found at: https://www.bls.gov/oes/current/oes_stru.htm.
3To begin a search for SOCs by industry, please see BLS at: https://www.bls.gov/oes/current/oessrci.htm.
4BLS maps of OES data may be found at: https://www.bls.gov/oes/current/map_changer.htm
5Be careful to adjust your SOCs for the changes in the system that occurred between 2010 and 2018. See a description of the changes at: https://www.bls.gov/soc/2010/home.htm.
7Non-Metropolitan data can be found at: https://data.bls.gov/oes/#/occGeo/One%20occupation%20for%20multiple%20geographical%20areas. County level data can be found at: https://data.bls.gov/PDQWeb/en. For county level data, the OES data is organized by industry.
8Labor force characteristics from the Community Population Survey can be found through BLS’s “Geographic Profile of Employment and Unemployment” searchable database found at: https://www.bls.gov/gps/home.htm#data.
9The Bureau of Economic Analysis defines Location Quotient at: https://www.bea.gov/faq/index.cfm?faq_id=478 and “A location quotient (LQ) is a measure of a region’s industrial specialization relative to a larger geographic unit (usually the nation). An LQ is computed as an industry’s share of a regional total for some economic statistic (earnings, GDP by metropolitan area, employment, etc.) divided by the industry’s share of the national total for the same statistic” found at: https://www.bea.gov/library/_pdf/regional_info.pdf.
10Location Quotients by OES by state, county or MSA may be found at: https://www.bls.gov/oes/current/map_changer.htm.
11For further information on each service, please refer to: http://www.chmuraecon.com/jobseq/real-time-intelligence/; and http://www.economicmodeling.com/2016/08/01/emsis-expanded-job-posting-analytics/.
12James Stinchcomb, “Exploring the Labor Market in Amazon HQ2 Finalist Cities”, Chmura Analytics (April 19, 2018) found at: http://www.chmuraecon.com/blog/2018/april/19/exploring-the-labor-market-in-amazon-hq2-finalist-cities/.