Abstract
A laborshed analysis examines the available workforce that flows from the surrounding communities into a nodal city. Iowa Workforce Development (“IWD”) currently uses employer survey data to create a laborshed study for the largest communities in each of Iowa’s 99 counties. IWD has surveyed 18,428 Iowans from July 2019 to April 2021 to ask how likely these Iowans are to change jobs if they are currently employed, or how apt they are to rejoin the labor force if they are presently unemployed, recently retired or are a homemaker. The likelihood of changing jobs or re-entering the workforce is modeled through a polytomous response logistic regression, using both demographic information and labor market characteristics obtained from the surveyed Iowans. In this study, prediction of individual potential job applicants, together with estimates of workers for each zip code in-commuting to a nodal city, are detailed. In particular, this study estimates the total number of individuals who are eager to change jobs or regain employment, called the Weighted Labor Force (“WLF”), for any desired laborshed in Iowa. The WLF computation is demonstrated for the Cedar Valley Laborshed, which consists of the nodal cities of Waterloo and Cedar Falls in northeastern Iowa.
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Data availability statement
The dataset of 18,428 surveyed Iowans is property of IWD, as considerable taxpayer money has been expended to collect it through the vendor SPPG + Essman Research. Furthermore, both the authors have signed confidentiality agreements with IWD to receive access to the data. Given the duel nature of IWD’s commitment to the taxpayers of Iowa together with its desire to be transparent to the public in its endeavors, requests for access to the 18,428 surveyed Iowan’s data can be performed by contacting either author. Any such request will be passed to Ryan Murphy at IWD for approval and dissemination of the data.
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Acknowledgements
The work of the authors has been supported, at least in part, by the Institute for Decision Making and Iowa Workforce Development. The authors thank Ryan Murphy and Katie Lippold from Iowa Workforce Development and Randy Pilkington with the University of Northern Iowa’s Business & Community Services.
Funding
The first author receives consulting money from Iowa Workforce Development (“IWD”) to update and improve the laborshed model. The Institute for Decision Making (“IDM”) at the University of Northern Iowa serves a liaison between IWD and the first author. An undergraduate statistics student is also supported, by IWD through IDM, to help the first author manage data, to update and improve the laborshed model. The second author currently is employed by the Institute of Decision Making at the University of Northern Iowa. Both the authors work hand-in-glove on the laborshed project.
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The first author has performed the bulk of the analyses and manuscript write up. The second author, who works for IDM and serves as a liaison between IWD and the first author, performed the rest. In particular, the second author has provided guidance as to what aspects of the laborshed project should be detailed in the paper, including laborshed selection and data management, together with editing the manuscript, itself, for accuracy.
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The authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article. Specifically, neither author has a financial or non-financial conflict of interest in having this manuscript published.
Ethical approval
All data collection was performed, in accordance with relevant guidelines and regulations, by SPPG + Essman Research under the supervision of IWD (as discussed in the manuscript in “Surveyed Iowans Data”). Neither author was involved, either directly or indirectly, in collecting any data discussed in this manuscript.
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Each of the 18,428 surveyed Iowan has provided his or her consent to their survey data being gathered by SPPG + Essman Research for IWD.
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Ecker, M.D., Conrad, D. Estimating labor commuting patterns using polytomous response logistic regression. SN Bus Econ 3, 195 (2023). https://doi.org/10.1007/s43546-023-00568-4
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DOI: https://doi.org/10.1007/s43546-023-00568-4