The COVID-19 public health emergency caused widespread economic shutdown and unemployment. The resulting surge in Unemployment Insurance claims threatened to overwhelm the legacy systems state workforce agencies rely on to collect, process, and pay claims. In Rhode Island, we developed a scalable cloud solution to collect Pandemic Unemployment Assistance claims as part of a new program created under the Coronavirus Aid, Relief and Economic Security Act to extend unemployment benefits to independent contractors and gig-economy workers not covered by traditional Unemployment Insurance. Our new system was developed, tested, and deployed within 10 days following the passage of the Coronavirus Aid, Relief and Economic Security Act, making Rhode Island the first state in the nation to collect, validate, and pay Pandemic

Technology may displace tens of millions of workers in the coming decades. Part of the explanation for the projected displacement is an expanding mismatch in skills that employers seek and the skills that workers possess. Effects of labor force displacement disproportionately affect low-income workers and workers within industries where technological change replaces labor. As a result, a great deal of emphasis is placed on training and reskilling workers to ease transitions into new careers. However, utilization of training programs may be below optimal levels if workers are uncertain about the returns to their investment in training. While the U.S. spends billions of dollars annually on reskilling programs and unemployment insurance, there are few measures of program effectiveness that workers and government can use to guide training investment decisions and ensure delivery of valuable reskilling and improved outcomes. In a nationwide conjoint survey experiment, we find job seekers prefer information on the value-added returns to earnings following enrollment in training and reskilling programs. We identify a clear demand for value-added measures. For every 10% increase in expected earnings, workers are 17.4% more likely to express interest in a training program. To meet this demand for information, governments can provide return on investment measures. Fortunately, the data to estimate these returns are available in state administrative data. We demonstrate a causal machine learning method that provides these missing causal estimates of value-added that workers prefer and that can provide correct incentives in the market for labor training. Focusing on a set of workforce training programs in Rhode Island, our causal machine learning estimates suggest that training increases enrollees’ future quarterly earnings by \$605. We estimate that return on investment ranges between -\$1,570 in quarterly earnings for the lowest value-added program to \$3,470 in quarterly earnings for the highest value-added program.