Technological progress in the form of automation boosts productivity, but can cause adverse labor market outcomes for transitional generations. I study the role of unions, and labor adjustment costs that firms face more broadly, in shaping the evolution of wages and employment of workers exposed to labor replacement during the automation transition. Using variation across local labor markets in the U.S. since 1980, I first document that unionization gives rise to intergenerational redistribution by shifting the incidence of wage and employment decline from older, incumbent to young, incoming cohorts. Moreover, unions accelerate the overall employment decline in automating occupations, thereby inducing faster labor reallocation. I develop a quantitative equilibrium model of technological change and unionization that jointly rationalizes the two empirical observations through the impact of union-imposed firing costs on firms' intertemporal choice how to optimally adjust 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 by lowering their layoff risk and wage decline. The impact is shifted to young workers, 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 which limits the welfare impact on them. The impact of high unionization spills over into non-adopting occupations as the accelerated reallocation of labor depresses wages there.

We build a unified quantitative theory of increasing life expectancy and income growth in the last two centuries, and the emergence and expansion of a modern health sector in the 20th century. To do so, we develop a two-sector overlapping generations model with endogenous and directed technical change in which income growth, life expectancy, the size of and technological progress in the health and the final goods sector are jointly determined in general equilibrium. The model interprets the historical record as three phases of a dynamic equilibrium in which households are initially poor and the quality-adjusted price of health goods is prohibitively high so that demand for them is non-existent, and life expectancy is short and stagnant. As technological progress starts to fuel income growth, households commence consuming basic health goods (such as a better diet and basic domestic hygiene), remaining life expectancy at age 20 starts to rise in the first half of the 19th century. 100 years later, further directed technological progress eventually leads to the emergence (in ca. 1940) and then the expansion of a modern health sector. In counterfactual analyses the model suggests that about 20% of the life-expectancy gains between 1940 and 2020 are attributed to increased spending in the modern health sector. Furthermore, public spending on health, in the form of subsidies to health R&D during WW II and subsequently, due to the emergence of Medicare, plays a major role in the kickoff of the modern health sector during WWII and its expansion afterwards.

This paper introduces higher-order earnings risk consistent with recent empirical findings into a benchmark heterogeneous-agent macro model to examine its implication for the distribution of wealth. I find that higher-order earnings dynamics induce higher earnings inequality driven primarily by persistent earnings losses at the bottom. Poor households respond by strongly cutting consumption leading to more consumption and less wealth inequality which reinforces the known issue of generating the empirically observed wealth dispersion in this class of models. In addition to lower overall consumption, the higher-order earnings moments, particularly excess kurtosis, are passed through to consumption dynamics of the poor. Both effects combined mean that those households are willing to pay up to 1.7% of permanent consumption to avoid higher-order earnings risk. Moreover, the latter effect induces consumption dynamics of the poor to be predominantly driven by idiosyncratic earnings changes which significantly reduces the correlation between their consumption and aggregate output. Since wealthier households are not affected strongly the implications for the aggregate dynamics of the economy are negligible. Methodologically, I develop a new General Polynomial Chaos Expansion approach to solve for the aggregate dynamics of this class of models, and contrast its efficiency with previous methods.

Benchmark finance and macroeconomic models appear to deliver conflicting estimates of the natural rate and bond risk premia. This natural rate puzzle applies not only in the U.S. but across many advanced economies. We use a unified no-arbitrage macro- finance model with two trend factors to estimate the natural rate r∗ for 10 advanced economies. We cover a longer and wider sample than previous studies and draw on new sources to construct yield curves and excess returns. The two-trend model improves the explanatory power of yield regressions and return forecasts. Most variation in yields is due to the macro trends r∗ and π∗, and not bond risk premia. Global components of unexpected bond returns are influential, while the local components of natural rates are large. Our r∗ estimates covary with growth and demographic variables in a manner consistent with theory and previous findings.