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IMPLAN Blog

Drill Down into Employment Impacts using Our New Occupational Data Matrices

September 13, 2018 by Nasera Kaouss

Being equipped with the proper data as well as the right analysis tools can help you make better and more informed business decisions. IMPLAN provides excellent data that can supplement your analyses. Our latest release of the Occupational Data Matrices joins the ranks of those supplemental data sets. This data set provides information on employment and compensation, which might just make determining wages and allocating scarce resources a less arduous task.

Made with the Best Ingredients

Before you adopt any data set, it is essential to know its source as well as how it was aggregated. IMPLAN uses multiple sources to gather our data, but the most notable one is the Bureau of Labor Statistics (BLS) Occupational Employment Statistics (OES) data, which is provided by Standard Occupational Classification (SOC) and North American Industry Classification System (NAICS) codes. Additional sources include BLS Employment Projections, BLS military employment data, and Census Public Microdata Sample (PUMS) data. After collecting all of the necessary and available data, our economists “fill in the blanks.” They do not make up any arbitrary numbers, but instead use the aforementioned data points and sets to infer what the missing data might be. It is important to note that missing data is usually due to government non-disclosure rules and regulations.

Our economists first use detailed occupation data from the BLS to resolve holes in the less detailed occupation data. They then use a top down method to finish disclosing the data sets. This means that they start with the most aggregated data and fill in the blanks all the way down to the most detailed industries and occupations while controlling to NAICS totals. Once disclosure is complete, industry specific ratios of employment by occupation and compensation by occupation are derived from the disclosed values. Their other outside sources and tools aid them in creating a solid data set for you to use in your analysis.

Getting Started

From your model, whether it comes from IMPLAN Pro or IMPLAN Online, you can choose to either use your study area data or your results if you ran a scenario. Before any distributions of employment and compensation by occupation are calculated, it is important to understand the purpose of each of the tools that you will be given. Your download comes with two spreadsheets, one is used to calculate values of employment and the other to calculate values of compensation. Both of these spreadsheets give you values by sector and occupation. You are also provided with two CSV files that you must use to adjust your employment value. IMPLAN employment includes both wage and salary workers and proprietors; however, the matrices apply to wage and salary employment only. These files provide the ratios for wage and salary employment by industry, by region.

Plugging in Your Data

Now it’s time to get a better understanding of what the data actually is and how to interpret it. When you gain access to this data, there are two separate spreadsheets which contain matrices for employment and compensation. Each spreadsheet contains rows of sectors and columns of occupations. It is essential to input the proper data and that can be found in either IMPLAN Online or IMPLAN Pro. This also includes adjusting for employment to remove proprietor employment. After gathering the necessary data, the values can be copied into the first column of the matrix. This will generate the rest of the employment values for all IMPLAN sectors and occupations.

It is important to note that these numbers may be miniscule, either due to accounting for the seasonal and part time employees, or more likely to the fact that one person performs multiple roles. A person accomplishing multiple jobs in this case does not work different, distinct jobs, but instead may have multiple job responsibilities for their one job and therefore will be accounted for across different occupations. Overall, this matrix provides data for the number of employees for a given occupation in a given IMPLAN sector.

Standard Occupational Classification Structure
The figure above gives a more detailed description of how the SOC codes are broken down. This is also a reference to the codes found in the spreadsheet to show how aggregated or detailed a certain occupation is. The most aggregated codes are referred to as major groups. These are then followed by minor, broad, and detailed in order of most aggregated to most detailed.

 

The second spreadsheet is set up the same way; however, instead of the numbers in the matrix representing numbers of employment, they represent compensation. Compensation is defined as including salary, wages, and benefits. These values do not need to be adjusted like employment. The values can be pulled directly from your study area data or from results if you chose to run a scenario. Just like for the employment matrices, the rest of the values throughout the matrix will be generated once the initial values are added. Each cell represents the compensation by sector and occupation. If you’re like me and would prefer a visual walk-through of how to employ our Occupational Data set for your own purposes, register for our webinar (linked below) on Thursday, September 20th at 2PM where our economists Mark and Jimmy will go into detail about the data, and how to use the spreadsheets.

Wrapping it Up

Whether you are using the study area data or the impacted data to input into your matrices, both can be useful. The resulting data can help you evaluate whether to support policy or inform your next economic impact analysis, among myriad other applications. This release improves upon IMPLAN’s prior Occupational Data release by including compensation in addition to employment. If you would like to learn more about our Occupational Data, register for our instructional, in-depth webinar on the topic “Occupational Matrices: Occupational Employment and Compensation by Industry” hosted by our economists James Squibb III and Mark Taylor.

 

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Topics: Data, Economics

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