We examine the role of technological change in explaining the large and persistent decline in earnings following job loss. Using detailed skill requirements from the near universe of online vacancies, we estimate technological change by occupation and find that technological change accounts for 45 percent of the decline in earnings after job loss. Technological change lowers earnings after job loss by requiring workers to have new skills to perform newly created jobs in their prior occupation. When workers lack the required skills, they move to occupations where their skills are still employable but are paid a lower wage.

We empirically establish that unemployed individuals maintain significant access to credit and that upon a layoff, the unconstrained borrow while the constrained default and delever. Motivated by these findings, we develop a theory of credit lines and labor income risk to analyze optimal transfers to the unemployed. Since credit lines offer fixed interest rates and limits, credit lines are unresponsive to layoffs and provide greater consumption insurance relative to when debt is repriced period by period. At US levels of credit lines, the government can optimally reduce transfers to the unemployed, whereas this is not true when debt is counterfactually repriced period by period.

For whom has earnings risk changed, and why? We answer these questions by combining the Kalman filter and EM algorithm to estimate persistent and temporary earnings for every individual at every point in time. We apply our method to administrative earnings linked with survey data. We show that since the 1980s, persistent earnings risk rose by 12.5 percent for both employed and unemployed workers and the scarring effects of unemployment doubled. At the same time, temporary earnings risk declined. Using education and occupation codes, we show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption.