This thesis consists of three chapters and studies the macroeconomicsof technological change and inequality, in particular the distributional consequences of technological and structural change. In the first chapter, I ask how labor adjustment costs, specifically in the formof unionization, shape the evolution of wages and employment of workers exposed to labor replacement by automation. I argue that, by raising adjustment costs, unions generate intergenerational redistribution by shifting the impact from existing, older to incoming, younger cohorts, and further generate aggregate effects by accelerating overall labor reallocation from automating to non-automating occupations. The reason is that labor adjustment costs incentivize firms to adjust through hiring rather than layoffs, and to reduce labor in anticipation of future adoption. Using variation across local labor markets in the U.S. since 1980, I document that unionization among exposed workers is associated with greater wage and employment decline among young relative to older workers, and with accelerated overall employment decline. I then develop an overlapping generations model of technological change and unionization that rationalizes the empirical findings through the impact of union-imposed labor adjustment costs on firms' choice how to transform their workforce over time when gradually adopting automation. Within automating occupations, unions reduce the welfare cost of automation of older workers along the transition by up to 4% of permanent consumption while raising the welfare costs of cohorts entering during the transition by up to 2%. Incoming workers endogenously respond to automation by entering non-adopting occupations. The union effect spills over into non-adopting occupations as the accelerated labor reallocation depresses wages there. The second chapter, joint with Dirk Krueger and Alexander Ludwig, poses thequestion what caused the increase in life expectancy since 1800 and the rapid growth of a modern health sector during the 20th century in the United States. We document the evolution of life expectancy over the last two centuries and the emergence of the modern health sector in the 20th century in the U.S. We then provide a quantitative theory of the joint dynamics of income growth, the modern health sector, and the increase in life expectancy over the last two centuries to explain the documented facts. The theory is built on the insight that the demand for health increases over time as individuals become richer and older, which in turn sparks a reallocation of resources towards the production and innovation of health goods. Households are initially too poor to demand health goods, and life expectancy is stagnant. As income grows, fueled by technological progress, households start consuming basic health goods, life expectancy starts to rise, and directed technological progress eventually, with a delay of 100 years, leads to the emergence of a modern health sector. We find that rising household demand for health accounts for one-third of the observed increase in the relative price of health goods, while two-thirds are accounted for falling input prices in the modern health sector relative to the final goods sector, driven by technological progress. Moreover, modern health goods have accounted for roughly 30% of the increase in life expectancy at age 20 since 1940, which translates into 3.3 additional expected years of life. In the third chapter, I study how much rising risk in labor earnings matter for wealth inequality and welfare. I answer this question by introducing higher-order earnings risk consistent with recent empirical findings into a benchmark heterogeneous-agent model. I show that higher-order earnings dynamics in the form of left-skewness and excess kurtosis strengthen the precautionary savings motive, leading to greater consumption inequality and lower wealth inequality. The earnings dynamics are partially passed through to the consumption of poor households who are willing to pay up to 1.7% of permanent consumption to avoid higher-order earnings risk. Methodologically, I develop a new General Polynomial Chaos Expansion approach, a global solution method to solve for the aggregate dynamics of this class of models, and demonstrate that it increases efficiency relative to previous methods. I extend the baseline method to allow for time-varying base distributions, which is particularly useful in economic settings in which the cross-sectional household distribution at times moves far away from the ergodic distribution. I then apply the extension by introducing time-varying earnings risk into the benchmark model.