Workforce demographics, site selection labor reports by Workforce Strategies, Inc.

Using the Data

These datasets are designed to give a more complete picture of a local day-time population (workforce). They data drill down into a geography revealing more about its strengths and weaknesses. Each of the datasets is intended to stand-alone - and - also to work side-by-side with other workforce data.

Giving you the edge, the demographics are used for market analytics, media planning, business development, proposal content, site selection modeling, and much more. In addition, they are created with the understanding that real estate and site selection projects are done in conjunction with local parties such as planners, economic developers, workforce development boards, trainers, recruiters, and more.

Using the Data: The most effective way to learn about local workforce and day-time patterns and a particular data element is to look at the estimated employment number, percent, and index (concentration) values together. As indicated, the index shows how concentrated the element is when compared to the US. (The US is equal to one.) The index value shows the percent of employment that is above or below the national average of 100. (For example, an index value of 103 shows that the local employment figure is 3 percent above the national average.) Besides looking at the index, it is valuable to also note the number of people employed (and percent) for whatever element you are reviewing (skill, occupation, or industry), because the employment number helps discover if the workforce has the skill and occupation numbers (capacity) to sustain and support a particular development project. So, for example, while the index and percent may be high, the number may be low, which could indicate there are not enough kinds of certain workers (occupation) and skills in the local area to support an initiative.

Day-time population (workers) counts in the occupation datasets are good indicators of what is going on in an area for a several reasons. These summaries by occupation reveal many things about a local workforce and talent base.

For example, in the case of a retail and restaurant location, they indicate potential purchasers in an area during daytime hours.

Or, in the case of building a new plant, professional, logistic facility, or more – they show the group of workers that will be drawn upon for hiring purposes in a given location. This kind of information is critical because ultimately it is those currently employed (day-time population) in a location who will generally fill new jobs. Basically, local employment is based on pulling from the same set of workers when a new operation begins. The workforce reports help determine if a local area can support a development endeavor and/or what skills training can be developed to bolster a local workforce as part of making an initiative successful. Saving valuable time in assessing labor markets, these workforce demographics support accountability when aligning real estate strategies and business decisions.

Industry Specific Data Sets: these datasets drill down into the local industry dynamics and in terms of competitive analyses shows industry employment, skills, occupation distribution breakdown, and more. Using the same kinds of examples described throughout the above, the data helps answer questions about the strengths of a location - specific to an industry initiative. For example, the healthcare dataset shows the distribution of all healthcare related occupations, and, when looking at the the elder living occupation profile one sees the kinds of occupations and workforce that support these type of facilities. This technique for reading the reports can be applied to all of the industry specific ones.

The workforce datasets are tools that can be used throughout the entire process from market analyses and site selection to the start of production and service delivery including recruitment, hiring, and training - in any type of realestate, economic development, or workforce intiative.

Using the Data

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