This page is part of the community impacts section and describes impacts on the economic cost of the opioid crisis by estimating lost wages.

New Dataset for 2019 - Current by Quarter is now available -
Quarterly Census of Employment and Wages (QCEW) 2019 - Current County Labor and Industry
The Quarterly Census of Employment and Wages (QCEW) dataset provides information about the number of establishments within a geographic area by industry as well as the average number of employees and average weekly wages paid. QCEW is the universe of employment covered under Pennsylvania’s unemployment insurance laws. QCEW employment is based on the location of the position not where the person resides.

The data submitted reflects the number of trainings of Pennsylvania workers offered by DCED and its affiliate programs in Fiscal Year 2015-2018

This indicator includes the estimated lifetime wages lost due to premature opioid-related mortality. This value is the sum of the product of the number of years of life lost due to premature opioid-related mortality and the average quarterly wages for county of residence—as reported by the Bureau of Labor Statistics—adjusted for inflation and then discounted.

Represents a comprehensive collection of occupational wage data available for Pennsylvania. Data are collected through the Occupational Employment Statistics program in cooperation with the U.S. Department of Labor’s Bureau of Labor Statistics. Occupational wage information can be used as a reference by educators, PACareerLink® staff, career counselors, Workforce Development Boards, economic developers, program planners, and others.

Technical Note
Occupational wages do not represent a time series. Due to the prescribed production methodology, current occupational wages are not comparable to previously published occupational wages.

The data represents the percent change in wages for an individual who has wages recorded in the Unemployment Compensation (UC) wage record file in the quarter in which they completed Industry Partnership training and wages found in the UC wage record file for that individual four quarters later. The change could be an increase or a decrease in wages. For example, if an individual completed training in the third quarter of 2013 and earned $5,000 in that quarter and earned $7,500 in the third quarter of 2014 the percent change for that individual would be 50%. The file incudes a count of all individuals who benefited from industry partnership training, the workforce development area of the industry partnership, the training program completed and the percentage change in wages per individual training. The top line of the file includes the overall percentage change for all trainings.

*The goal for Labor & Industry is based on receiving $10 million to fund Industry Partnerships.

This dataset is for Program Year 2013-2017 and will be updated annually due to federal release schedule.
There are many reasons why an individual’s wage may have changed dramatically. Some of the reasons for negative wage changes or large increases in wages are listed below (not an exhaustive list).
• An individual may have left the job, was laid off, or retired within the year after they were trained.
• An individual may have become ill and left work.
• An individual may have accepted a job in or moved to another state.
• An individual may have been working two jobs and switched to one, or vice versa.
• An individual’s hours may have been reduced/increased during a quarter.
• Overtime hours may have been reduced/increased during a quarter.
• An individual may have taken family leave.
• A bonus could have been paid right after training was completed.
• Wage records may not have been reported.
• An employer may have closed and laid off all of their employees.

Prior Dataset for 2016 - 2018 Annual available - Quarterly Census of Employment and Wages (QCEW) 2016 - 2018 County Labor and Industry
The Quarterly Census of Employment and Wages (QCEW) dataset provides information about the number of establishments within a geographic area by industry as well as the average number of employees and average weekly wages paid. QCEW is the universe of employment covered under Pennsylvania’s unemployment insurance laws. QCEW employment is based on the location of the position not where the person resides.

The data depicts each training opportunity completed by individuals through Industry Partnership training funding by Program Year (PY). The file includes all training and the number of individuals that benefited from the training and the workforce development area in which the industry partnership is organized. The data show the amount of training that is driven by employer demand to ensure PA’s employers remain competitive and workers retain employment and enhance their career opportunities.

This is Department of Labor and Industry(DLI) dataset.
There are 5 other Workforce training files from Department of Community and Economic Development (DCED) that when combined with this file support the Governor's Workforce Development Goal of training 340,000 individuals by 2020

This indicator includes the number of days hospitalized for opioid use and the estimated lost wages due to hospitalization (PHC4 Data). This value is the product of the number of days hospitalized and the average weekly wages for county of residence—as reported by the Bureau of Labor Statistics—divided by seven.
PHC4’s database contains statewide hospital discharge data submitted to PHC4 by Pennsylvania hospitals. Every reasonable effort has been made to ensure the accuracy of the information obtained from the Uniform Claims and Billing Form (UB-82/92/04) data elements. Computer collection edits and validation edits provide opportunity to correct specific errors that may have occurred prior to, during or after submission of data. The ultimate responsibility for data accuracy lies with individual providers.
Analyses were completed by the University of Pittsburgh using data from the PA Health Care Cost Containment Council and in cooperation with PA DOH.

This indicator includes the number of days hospitalized for Opioid Use Disorder and the estimated lost wages due to hospitalization.
This data view is filtered by Annual date and All Ages for All genders based from the original dataset.