The proliferation of statewide integrated data systems linking data across state agencies provides opportunities to examine the efficacy of state programs. Since all states run Unemployment Insurance (UI) compensation programs, wage data collected from employers can be a valuable outcome measure. However, research using a state’s UI wage records can introduce biased results if an analyst is not careful. This paper examines the different methods for computing annual wages using UI wage records and attempts to identify the method that yields the lowest bias.
Applied Data Analytics Course Papers and Presentations
From April to July 2021, New Jersey participated in the Coleridge Initiative’s Applied Data Analytics virtual course. Researchers and state agency staff from within New Jersey and other states participated in the course to gain real-world experience in data analytics through big data tools such as SQL and R, linking administrative records, and developing data visualizations.
The practical components of the course utilized data from New Jersey’s Office of the Secretary of Higher Education and Department of Labor and Workforce Development. These data included postsecondary enrollments and completions, and data on wages and employer. The class culminated in several small groups developing working papers and presentations based on analyses conducted for the class. Those papers are presented here in their original format, and cover the following topics:
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There are several limitations to these analyses that are important to note. These analyses were limited to data from New Jersey and excludes data from those who move out of the state for education or employment. Results should be interpreted with caution as estimates do not include graduates who are working out-of-state. Additionally, state data sources may be limited in scope. For higher education, data are presented for graduates of New Jersey colleges and universities participating in the SURE data collection, which are predominately public institutions. Employment is measured using state Unemployment Insurance (UI) wage records, which capture the employment experiences of most in-state employees, including all employees of private firms participating in the UI program, but excludes non-participating federal, military, agricultural, and self-employed workers.
Despite these limitations, these working papers present early case studies of the important lessons that can be gained from analyzing longitudinal administrative data. Future opportunities to analyze this data will include a small grant program to external researchers, an internship program, and future iterations of the ADA course. New Jersey looks forward to promoting access to this data and its use to strengthen the foundation of evidence-based policymaking in the state.
Readers should note that each of these products are either a working paper or presentation and is released to inform interested parties of research and to encourage discussion. Working papers represent research that is in progress. The views expressed are those of the authors and are not necessarily meant to represent the position or opinions of the New Jersey Education to Earnings Data System, its agency partners, the State of New Jersey, Rutgers University, nor the official position of any staff members. The authors accept responsibility for any errors.
This report describes how scholars used data from the New Jersey Motor Vehicle Com- mission to link 82% of secondary school exiters (18 or older) between 2011 and 2015 with the State of New Jersey’s higher education enrollment and Unemployment Insurance wage data