Imputation Rules to Improve the Education Variable in the IAB Employment Subsample

The education variable in the IAB employment subsample has two shortcomings: missing values and inconsistencies with the reporting rule. We propose several deductive imputation procedures to improve the variable. They mainly use the multiple education information available in the data because the employees' education is reported at least once a year. We compare the improved data from the different procedures and the original data in typical applications in labor economics: educational composition of employment, wage inequality, and wage regression. We find, that correcting the education variable: (i) shows the educational attainment of the male labor force to be higher than measured with the original data, (ii) gives different values for some measures of wage inequality, and (iii) does not change the estimates in wage regressions much

Should UI Eligibility Be Expanded to Low-Earning Workers? Evidence on Employment, Transfer Receipt, and Income from Administrative Data

Recent efforts to expand unemployment insurance (UI) eligibility are expected to increase low-earning workers’ access to UI. Although the expansion’s aim is to smooth the income and consumption of previously ineligible workers, it is possible that UI benefits simply displace other sources of income. Standard economic models predict that UI delays reemployment, thereby reducing wage income. Additionally, low-earning workers are often eligible for benefits from means-tested programs, which may decrease with UI benefits. In this paper, we estimate the impact of UI eligibility on employment, means-tested program participation, and income after job loss using a unique individual-level administrative data set from the state of Michigan. To identify a causal effect, we implement a fuzzy regression discontinuity design around the minimum earnings threshold for UI eligibility. Our main finding is that while UI eligibility increases jobless durations by up to 25 percent and temporarily lowers receipt of cash assistance (TANF) by 63 percent, the net impact on total income is still positive and large. In the quarter immediately following job loss, UI-eligible workers have 46-61 percent higher incomes than ineligibles