In response to an adverse labor-market shock, a calibrated heterogeneous-agent model predicts that aggregate spending on durable goods falls mainly due to the ex-ante increase in income uncertainty caused by higher unemployment risk. In contrast, aggregate spending on nondurable goods falls mainly due to the ex-post income losses associated with realized unemployment spells. When households hold little liquid assets, the nondurable spending response is amplified, whereas the durable spending response is dampened. These differences stem from micro-level adjustment frictions involved in purchases of durable goods. The model is corroborated with evidence from micro survey data.

Atkinson et al. (2018) propose a measure of the glass ceiling exploiting that top incomes are approximately Pareto distributed. We clarify how this glass-ceiling coefficient describes the increasing scarcity of women further up in the income distribution and show how it relates to the top-income gender gap. If interpreting top income gender differences as caused by a female-specific income tax, the gender gap and glass-ceiling coefficient measure its level and progressivity, respectively. Using Danish data on earnings, we show that the top gender gap and the glass-ceiling coefficient evolves differently across time, the life cycle, and educational groups.

I introduce a method for simulating aggregate dynamics of heterogeneous-agent models where log permanent income follows a random walk. The idea is to simulate the model using a counterfactual permanent-income-neutral measure which incorporates the effect that permanent income shocks have on macroeconomic aggregates. With the permanent-income-neutral measure, one does not need to keep track of the permanent-income distribution. The permanent-income-neutral measure is both useful for the analytical characterization of aggregate consumption-savings behavior and for simulating numerical models. Furthermore, it is trivial to implement with a few lines of code.

Following the recent disruption in production due to COVID-19, we investigate whether temporary adverse shocks can result in persistent demand-driven recessions through sluggish labor-market dynamics. We consider an incomplete-markets model with sticky prices and search frictions, and show how introducing sluggish vacancy creation and endogenous layoffs gives rise to a powerful and persistent feedback loop between unemployment risk and aggregate demand. Endogenous layoffs are central because they generate a rapid rise in unemployment following a temporary shock. Sluggish vacancy creation is central because it implies that job-finding rates remain persistently low following the surge in layoffs. As a result, the negative feedback loop continues even after the initial shock dies out. The feedback mechanism is weak in the corner cases of either free entry, exogenous separations or complete markets. The model provides justification for using match-saving subsidies to stabilize the business cycle.

I introduce a simple model which endogenously generates a Pareto distribution in top incomes, consistent with empirics. Workers inhabit different niches, and the income of a worker is determined by the niche-specific supply of labor and a constant-elasticity labor demand curve. The highest paid workers are the ones that inhabit a niche with few other workers. A Pareto tail in incomes emerges as long as the distribution of workers over niches satisfies a regularity condition from extreme-value theory, satisfied by virtually all continuous distributions in economics.

We use Danish administrative data 1980-2013 to study the underlying mechanisms generating fluctuations in income risk. We partition the population into 37 narrowly defined educational categories and document the cyclicality of labor income risk for each category separately. For the individual educational categories, mean income growth is strongly correlated with income growth skewness, with an average correlation of 0.87−0.88. We show that the connection between income growth skewness and mean income growth is not only strong in the time dimension, but also in the cross section. Across the 37 educational categories, the correlation between mean income growth and income growth skewness is 0.93−0.96. We show that labor-market frictions together with variations in productivity growth generate the relationship between mean income growth and income growth skewness. In a quantitative job-ladder model, variations in productivity growth quantitatively capture both the time-series and cross-sectional relationship. In contrast, variations in the job-finding rate, the job-separation rate and the offer-arrival rate for employed fail to generate the relationship between mean income growth and income growth skewness in our framework.