Methodology for Defining Workforce Regions in the State System s Gap Analysis

Transcription

1 CLARION UNIVERSITY Methodology for Defining Workforce Regions in the State System s Gap Analysis The purpose of this document is to lay out the methodology for defining the University Workforce Regions in the Gap Analysis. The university workforce regions developed in this update build upon the regions developed in the Gap Analysis project. All regions retained the counties identified from the project. All counties that met the criteria described below were added to the existing university workforce regions. Alumni settlement rates are new to the Gap Analysis project and provided additional insights into the workforce regions that the State System universities support. Defining the University Workforce Regions This process involves economic modeling approaches that include Central Place Theory, workforce flows, economic leakage, origin of learners, and learner destination after completing degrees to define a university workforce region. A workforce region (or labor shed) is defined as a labor market area where residents can find jobs within a reasonable commuting distance or can change their employment without changing their place of residence. Given the target audience for the workforce region State System universities the context used to define the regions reflects multiple regional characteristics that the universities are tasked to support, while still conforming to the broader definition of a functional economic market area (FEMA). A FEMA is best described as an area that contains key economic markets. These economic markets reflect drivers of the local economy such as the workforce and inter-industry business activity. The workforce regions are defined at the county level, starting with the county in which the university is located. For this analysis, four primary drivers are measured to best define workforce regions, specifically: 1. Workforce Flows: measured through Census LEHD commuting patterns, this captures the rate of in-commuters and out-commuters from a university region. 2. Learner Origin Data: measured by the proportion of State System learners from a given county who are attending a specific State System university and the proportion of learners from a given county who attend a specific State System university. 3. Alumni Settlement : measured by the proportion of State System graduates from a given university who settle in a particular county. 4. Additional Considerations: To provide universities with the opportunity to include additional counties in their respective workforce regions, the State System considered counties that did not qualify under the above criteria if there was supplemental data to support inclusion. To ensure that a region can be considered a functional economic market area (FEMA), input-output modeling measured inter-industry exchange and the ability to satisfy household demand for 1

2 goods and services in the region. Geographic Considerations To define regions for State System universities, county-level data provides the best units of economic and workforce information. As such, county groupings serve as the best structure for describing the regions. However, some universities are located in areas that are near large metropolitan areas, while other universities are not. Therefore, a blanket approach of setting threshold primary driver values when defining a workforce region for each university ultimately favors universities located near large metropolitan areas. The State System seeks to include all counties in Pennsylvania as part of the Rising to the Challenge Strategy. Given the small population and workforce counties that exist across Pennsylvania, combined with many State System universities located within geographic proximity of these small counties, the three specified drivers are analyzed at the county level to allow for broad geographic coverage. Furthermore, the Gap Analysis focused on counties located in Pennsylvania. The Gap Analysis expands the geographic coverage by considering contiguous counties in border states that meet the threshold for inclusion in a university workforce region. Process Given the workforce/commute, learner, and economic variances that exist within the different geographies across Pennsylvania, an iterative process is used to define the workforce region, starting with the county in which the university is located. This multiple-step, iterative process seeks to maximize the primary driver values (workforce flows, economic activity, learner capture rate, and learner destination), while adhering to the guiding principles of a functional economic area AND also ensuring that each county in Pennsylvania is assigned to at least one university workforce region. The process is broken into the following steps for each driver, as described below. 1. Workforce Flows Defined by commuting patterns, workforce flows represent the flow of workers into and out of the region, internal flow of people who both live and work in the area, using the US Census Longitudinal Employer-Household Dynamics 2014 data. When defining the workforce region for a university, the analysis begins with the county in which the university is located, followed by: 1. Identification of the top counties (by percent) of in-commuters, using a cutoff of 7% for in-state commuters and 5% for out-of-state commuters. 2. Identification of the top counties (by percent) of out-commuters, using a cutoff of 7% for instate commuters and 5% for out-of-state commuters. To provide a better understanding of commuting flows, the following figure for Allegheny County illustrates the analysis. 2

3 Fig. 1 Allegheny County inflow/outflow job counts, 2013 Source: U.S. Census Bureau Longitudinal Employment and Housing Dynamics The map indicates that 676,041 people work in Allegheny County (221, ,078). Of those, 454,078 people live and work in the county. Additionally, 221,963 workers commute into Allegheny County for work (i.e. in-commuters). This is represented by the darker green arrow on the left of the figure. When these numbers are converted into percentages, we find that 67.2% of people who work in Allegheny County live in Allegheny County: 454,078/(454, ,963) = 67.2%. Another way to describe this is that nearly one-third of the jobs in Allegheny County are filled by workers who live outside of the county. Furthermore, the map also indicates that 556,952 workers live in Allegheny County. Of those, 102,874 residents commute outside of Allegheny for their jobs (i.e. out-commuters). This is represented by the lighter green arrow on the right of the figure. When these numbers are converted into percentages, we find that 18.5% of people who live in Allegheny County work outside of the county (102,874/(454, ,874) = 18.5%). Another way to describe this is 81.5% of Allegheny County s employed residents live and work in the county. 2. Learner Origin This set of criteria helps to shape the regional backdrop of learners supported within a contiguous area around a university, using Fall 2014-Fall 2016 student enrollment data from the State System. Based on enrolled student headcounts, this analysis is specifically designed to broaden the contiguous region to help ensure inclusiveness of other, potentially low-population counties. Counties are selected based on two criteria for learner origin: county capture rate and university capture rate. Note that counties must be contiguous in order to conform to the definition of a workforce region. I. County Capture : This metric is calculated as each State System university s share of a given county s State System learners. For example, about 75% of State System learners from Erie County attend Edinboro University. Therefore, Edinboro s county capture rate for Erie County is 75%. Although a particular county may not contribute a large proportion of total State System students, this method ensures inclusiveness based upon each respective county s relative contribution. A county was assigned to a university workforce region if the university had the highest county capture rate for that particular county OR the county capture rate was greater than 20% for the particular university. 3

4 II. University Capture : This metric is calculated as each county s share of a given State System university s total enrollment. For example, 45% of Edinboro s students come from Erie County, so Edinboro s university capture rate for Erie County is 45%. This analysis ensures that a county could be included in a university workforce region even if the greatest share of that county s students do not attend that university. A county was assigned to a university workforce region if the county had a university capture rate greater than 10%. 3. Alumni Settlement s This set of criteria helps to shape the regional backdrop of alumni who find work within a contiguous area around a university, using the Emsi Alumni Insight database. A State System university s alumni settlement rate (ASR) for a given county is the proportion of that university s alumni who settle within the given county. For example, if 20% of West Chester University alumni settle in Chester County, West Chester s ASR for Chester County is 20%. The selection criteria for a county to be included in a university workforce region is a 7% ASR for in-state counties and a 5% ASR for out-of-state counties. Note that counties must be contiguous in order to conform to the definition of a workforce region. 4. Additional Considerations To provide universities with the opportunity to include additional counties in their respective workforce regions, the State System considered counties that did not qualify under the above criteria by checking whether they met any of the following criteria: I. The university has a satellite campus in the proposed county. II. The university grants degrees in conjunction with another university in the county. In this case, the university must be able to provide data on completions. III. The university has a large share of enrollment from an out-of-state border county, greater than 10% of total enrollment. In this case, the university must be able to provide data showing the share. To provide additional context of regional economic activity and ensure that a final regional description can be considered a functional economic market area (FEMA), input-output modeling is used to measure the level of inter-industry exchange and ability to satisfy household demand for goods and services in the region, using data from IMPLAN. An input-output model is an inter-industry accounting framework, wherein a proportion of demand for inputs by one industry is satisfied through the production of an output by another industry within a supply chain. Regions with well-developed supply chains and integrated markets typically retain a higher proportion inter-industry trade, thus reducing the level of economic leakage. Similarly, the ability for a region s businesses to satisfy the demand for household consumption reduces economic leakage. This analysis considers both inter-industry exchange (called Output Supply/Demand ) and household consumption (called Household Supply/Demand). After each workforce region was identified using the criteria for commuting and learner origin/destination, the gross commodity demand is calculated for that region using IMPLAN. Gross commodity demand is the total demand for supply-chain products and services by businesses in the region. The Output Supply/Demand ratio represents the percent of industry demand for supply-chain products and services that are satisfied by businesses in the region. The Household Supply/Demand ratio represents the percent of household demand for goods and services that are satisfied by businesses in the region. The final Gap Analysis workforce regions are detailed on the next page. 4

5 University Bloomsburg Workforce Region Counties County Count Out- Commuters In- Commuters Student Capture University Capture Alumni Settlement Business Demand Household Demand Columbia, Luzerne, Montour, Northumberland, Schuylkill, Snyder, Sullivan, Union, Lackawanna, Dauphin, Lehigh, Lycoming, Carbon % 82.5% 32.4% 45.4% 42.6% 56.6% 86.7% California Cheyney Clarion East Stroudsburg Washington, Allegheny, Fayette, Greene, Westmoreland, Somerset % 83.1% 36.6% 71.7% 77.6% 69.4% 92.5% Chester, Delaware, Montgomery, Philadelphia, New Castle (DE) % 79.0% 2.3% 83.1% 91.3% 72.9% 92.6% Clarion, Armstrong, Butler, Elk, Forest, Jefferson, Venango, Allegheny, Clearfield % 79.4% 21.0% 57.3% 58.3% 67.3% 91.2% Monroe, Carbon, Lackawanna, Northampton, Pike, Wayne, Lehigh % 77.7% 34.5% 64.6% 53.6% 58.2% 86.3% Edinboro Erie, Crawford, McKean, Warren % 86.4% 67.2% 59.1% 43.5% 45.9% 85.5% Indiana Kutztown Lock Haven Indiana, Allegheny, Armstrong, Bedford, Blair, Cambria, Jefferson, Somerset, Westmoreland, Cameron, Elk % 80.8% 40.6% 50.1% 55.2% 67.1% 90.8% Berks, Bucks, Chester, Lehigh, Montgomery, Northampton, Schuylkill, Philadelphia, Carbon, Lancaster % 86.7% 17.4% 78.2% 86.3% 69.7% 89.6% Clinton, Cameron, Centre, Clearfield, Juniata, Lycoming, Mifflin, Huntingdon, Dauphin % 77.8% 32.6% 43.6% 36.7% 53.1% 84.9% Tioga, Bradford, Potter, Susquehanna, Wyoming, Chemung Mansfield (NY), Steuben (NY), Lycoming % 79.7% 37.6% 50.1% 43.7% 47.3% 79.7% Millersville Lancaster, Chester, Dauphin, Lebanon, York % 82.6% 40.9% 62.6% 56.2% 61.6% 85.9% Cumberland, Adams, Dauphin, Franklin, Fulton, Huntingdon, Shippensburg Perry, York, Bedford, Juniata % 71.3% 34.2% 53.3% 48.7% 54.8% 83.7% Slippery Rock Butler, Allegheny, Beaver, Lawrence, Mercer, Westmoreland % 81.6% 34.2% 64.0% 74.0% 68.5% 91.9% West Chester Chester, Bucks, Delaware, Montgomery, Philadelphia, New Castle (DE) % 79.4% 45.3% 77.4% 86.3% 72.6% 92.2% 5