This story’s analysis of the effects of job-retraining in Janesville, Wis., is the first in the United States that has examined this question using data since the recent recession made jobs harder to find. It is patterned after a few older studies elsewhere that used similar methods to identify dislocated workers who went back to school, examine […]
This story’s analysis of the effects of job-retraining in Janesville, Wis., is the first in the United States that has examined this question using data since the recent recession made jobs harder to find. It is patterned after a few older studies elsewhere that used similar methods to identify dislocated workers who went back to school, examine how much they were working and earning afterwards, and compare them with a group of dislocated workers who had not been retrained. The analysis was performed in collaboration with two labor economists, Kevin Hollenbeck, a senior economist at the Upjohn Institute for Employment Research in Kalamazoo, Mich., and Laura Dresser, associate director of the Center on Wisconsin Strategy at the University of Wisconsin-Madison.
We focused on students who retrained at Blackhawk Technical College, a two-year, state-supported institution that attracts the vast majority of laid-off workers in south central Wisconsin who go back to school. Several kinds of raw data went into the analysis. The Wisconsin Department of Workforce Development provided two datasets. First, to identify people who were unemployed, we used the department’s records of unemployment claims from the summer of 2008 to the fall of 2011, for residents of Rock County, Wis. (Janesville is the county seat) and neighboring Green County. Most of Blackhawk’s students come from those counties, and we wanted to be able to compare the students to other jobless people in the same places. The second data set from the department contained Unemployment Insurance wage records, a kind of data kept by every state of each employee’s wages that all employers are required to report. The records show quarterly earnings, in other words, what the employee is paid over three-months. We used wage records for the same two counties. We also used records from Blackhawk, provided by Michael Gagner, the college’s director of institutional effectiveness. The Blackhawk records consisted of all students who enrolled in credit programs between the summer of 2008, when large number of jobs began to disappear from Janesville, and the summer of 2010. We stopped with 2010 so that the students in our analysis would have had time to finish their schooling and look for a job. The student records contained basic demographics, such as age, sex, race and ethnicity, as well as academic information, including whether they needed remedial work, the academic program they pursued, whether they graduated.
None of the records identified individuals by name. All three datasets – the unemployment claims, wage records, and college records – contained Social Security numbers. We used these to link the datasets. This linking was performed by Matias Scaglione, a labor economist in the Department of Workforce Development’s Office of Economic Advisors. We identified Blackhawk’s dislocated workers primarily by identifying students who had received unemployment benefits at some point during the period we examined. We identified others from a questionnaire that Blackhawk gives all new students that asks, among other things, their employment status. Students who answered that they were “unemployed” or “dislocated” were included in the analysis. By comparing Social Security numbers, we made certain that no student was counted more than once.
Once we had identified the dislocated workers who were retrained, we did several kinds of analysis. We created a pre-recession (and pre-layoff) period of 2007 and compared that with a “post” period of the final year for which we had information. In this way, we compared how many had any wages—that is, were working for pay—before and after they retrained. We could not identify whether people had full-time or part-time jobs, so we divided them into “consistent workers,” who had some earnings each quarter of the year; “intermittent workers,” who had at least one quarter with earnings and one with no earnings; and people who had no reported earnings.. The data contained earnings only from Wisconsin, not from any other state, but other information suggests that relatively few people in the area have jobs elsewhere. We also compared their “pre” wages with those afterwards. And we compared all these results with those from the group of unemployed people in Rock and Green counties who had not gone to the college. We also compared the academic performance of the college’s dislocated workers with that of other students on campus at the same time. The analysis could not control for potential differences in “quality” between the dislocated worker who were retrained and the other group of unemployed people, in large part because there was no way to gauge the education levels of the group that did not undergo retraining.
Amy Goldstein is a staff writer on leave from the Washington Post, focusing on Janesville, Wis., as a microcosm of the effects of vanished jobs on people and the places where they live. The Joyce Foundation, Harvard University’s Radcliffe Institute for Advanced Study, the Woodrow Wilson International Center for Scholars, the University of Wisconsin-Madison Institute for Research on Poverty, and ProPublica have provided support. She can be reached at email@example.com.
This story was originally published by ProPublica.