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Resident Workers/Occupation by Residence Data
Resident Workers/Occupation by Residence Data
Updated over a week ago

Unlike the majority of Lightcast data, resident worker data is presented in terms of where workers live rather than where they work. For instance, though ZIP code 85042 might have 50 software developers working in the region, there might only be 25 software developers who live there. This data is helpful in demonstrating workforce availability and helping companies locate the talent they need.

Source: This data comes from the Census’ LODES program and is most commonly used in their On The Map tool. Within the LODES dataset, Lightcast makes particular use of the Origin and Destination (OD) data, Regional Area Characteristics (RAC), and Workforce Area Characteristics (WAC) data to produce occupation by residence data.

Methodology Overview: QCEW is the foundation of Lightcast employment data for both industries and occupations. This is because the US lacks a comprehensive census-based (administrative records as opposed to surveys) source for occupation data, so Lightcast produces occupation data by running industry data through a regional staffing pattern derived from the OEWS survey data. This essentially uses the strengths of all available data, the numerical accuracy of QCEW and the less reliable occupation detail of OES to create a synthetic dataset of detailed occupational estimates. To create occupation by residence data, Lightcast also includes LODES as an input to the model to first convert industry data from place of work to place of residence before applying the staffing pattern to generate occupation data. LODES can lag behind other sources by 2-3 years, so we create a commuting pattern specific to each year of Lightcast employment data to model the employment from place of work to place of residence. The commuting pattern is adjusted to the matching year of industry employment before being applied (e.g.: to produce 2020 occupation by place of residence data, the industry commuting pattern from the closest year of LODES data is adjusted to match the 2020 industry data and is then run through a 2020 staffing pattern).

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