We construct a framework for integrated epi-econ assessment: theory, calibration to micro-, macro-, and epi-data, and numerical methods for quantitative policy evaluation. The model has an explicit microeconomic, market-based structure. It highlights trade- offs, within period and over time, associated with activities that involve both valuable social interaction and harmful disease transmission. We compare market solutions with socially optimal allocations. Our calibration to covid-19 implies that households shift their leisure and work activities away from social interactions. This is especially true for older individuals, who are more vulnerable to disease. The optimal allocation may or may not involve lockdown and changes the time allocations significantly across age groups. In this trade-off, people’s social leisure time becomes an important factor, aside from deaths and GDP. We finally compare optimal responses to different viruses (SARS, seasonal flu) and argue that, going forward, economic analysis ought to be an integral element behind epidemiological policy.

We quantify the unemployment-risk channel in business-cycle fluctuations, whereby an initial contractionary shock is amplified through workers reducing their demand in fear of unemployment. We document two stylized facts on how unemployment and unemployment risk respond to identified demand and supply shocks in US data. First, separation and job-finding rates play similar important roles in accounting for the overall unemployment response. Second, separations are more important early on, while job-finding rates respond with a lag. We show how a tractable heterogeneous-agent new-Keynesian model with a frictional labor market matches both facts once we include endogenous separations and sluggish vacancy creation. Relative to a model with exogenous separations and free entry, our framework attributes almost twice as large a share of output fluctuations to the inefficient unemployment-risk channel, and thus gives a larger role to stabilization policy.

I introduce a simple model which endogenously generates a Pareto distribution in top earnings, consistent with empirics. Workers inhabit different niches, and the earnings 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 earnings 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 adapt the wage contracting structure in Chari (1983) to a dynamic, balanced-growth setting with recontracting as in Calvo (1983). The resulting wage-rigidity framework dampens income effects in the short run, thus allowing significant responses of hours to aggregate shocks. In reduced form, the model dynamics are similar to that in Jaimovich and Rebelo (2009), with their habit parameter replaced by our probability of wage-contract resetting. That is, if wage contracts are reset frequently, labor supply behaves in accordance with King, Plosser, and Rebelo (1988) preferences, whereas if they are never reset, we obtain the setting in Greenwood, Hercowitz, and Huffman (1988).

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.

Extending the work of Atkinson et al. (2018), we decompose top-earnings gender disparities into a glass-ceiling coefficient and a top-earnings gender gap. The decomposition uses that both male and female top earnings are Pareto distributed. If interpreting top-earnings gender disparities as caused by a female-specific earnings tax, the top-earnings gender gap and glass-ceiling coefficient measure the tax level and tax progressivity, respectively. Using Danish data on earnings, we show that the top-earnings gender gap and the glass-ceiling coefficient evolve differently across time, the life cycle, and educational groups. In particular, while the top-earnings gender gap has been decreasing in Denmark over the period 1980-2013, the glass-ceiling coefficient has been remarkably stable.

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.

We discuss what tools would be useful in confronting epidemics, especially from the perspective of economics. Our main proposal is for policymakers to employ “epi-econ IAMs”: explicit Integrated Assessment Models, where epidemiology is integrated with economics. These models are under rapid development, but arguably not yet quite ready for quantitative use.

I use Danish administrative data 1980-2013 to study the underlying mechanisms generating fluctuations in income-shock moments. I partition the population into 37 narrowly defined educational categories and document the cyclicality of labor-income shock moments 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. 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. Labor-market frictions together with variations in productivity growth can 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.